Add 7 new benchmark suites for Gemma 4 comparison
New suites: - MMLU-Pro: Professional-level 10-choice QA (14 subjects) - GPQA-Diamond: Graduate-level science QA - BigBench Extra Hard: Challenging reasoning tasks - MMMLU: Multilingual MMLU across 10 languages - HLE: Humanity's Last Exam (extremely hard) - Tau2: Tool-augmented reasoning (retail/airline/finance) - Codeforces: Competitive programming with ELO scoring Updates: - AIME now supports aime_2026.jsonl for 2026 problems - Registry expanded to 17 suites in 5 categories - download_datasets.py supports HF downloads for new suites - README.md updated with full documentation Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This commit is contained in:
Abdelrahman Abdallah
+70
-30
@@ -37,11 +37,18 @@ python3 -m benchmarks.run_suite --all -o results.json
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| **SWE-Bench** | Coding | 5 | Resolve real-world GitHub issues |
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| **Aider** | Coding | 6 | Code editing and refactoring tasks |
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| **LiveCodeBench** | Coding | 5 | Competitive programming problems |
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| **Codeforces** | Coding | 10 | Competitive programming with ELO rating |
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| **MATH** | Math | 15 | Competition mathematics problems |
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| **GSM8K** | Math | 15 | Grade school math word problems |
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| **AIME** | Math | 10 | Challenging competition math (integers 0–999) |
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| **IFEval** | Instruction Following | 10 | Verifiable instruction-following evaluation |
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| **BFCL** | Instruction Following | 7 | Function/tool calling evaluation |
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| **MMLU-Pro** | Knowledge | 10 | Professional-level multiple-choice QA (10 choices) |
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| **GPQA-Diamond** | Knowledge | 10 | Graduate-level science QA (diamond difficulty) |
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| **MMMLU** | Knowledge | 10 | Multilingual MMLU across languages |
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| **HLE** | Knowledge | 10 | Humanity's Last Exam (extremely hard) |
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| **BigBench-Hard** | Reasoning | 10 | BIG-Bench Extra Hard reasoning tasks |
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| **Tau2** | Reasoning | 10 | Tool-augmented reasoning (retail/airline/finance) |
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### Commands
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@@ -60,12 +67,23 @@ python3 -m benchmarks.run_suite --suite mbpp
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python3 -m benchmarks.run_suite --suite swe-bench
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python3 -m benchmarks.run_suite --suite aider
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python3 -m benchmarks.run_suite --suite livecodebench
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python3 -m benchmarks.run_suite --suite codeforces
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# Math benchmarks
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python3 -m benchmarks.run_suite --suite math
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python3 -m benchmarks.run_suite --suite gsm8k
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python3 -m benchmarks.run_suite --suite aime
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# Knowledge benchmarks
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python3 -m benchmarks.run_suite --suite mmlu-pro
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python3 -m benchmarks.run_suite --suite gpqa-diamond
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python3 -m benchmarks.run_suite --suite mmmlu
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python3 -m benchmarks.run_suite --suite hle
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# Reasoning benchmarks
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python3 -m benchmarks.run_suite --suite bigbench-hard
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python3 -m benchmarks.run_suite --suite tau2
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# Instruction following benchmarks
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python3 -m benchmarks.run_suite --suite ifeval
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python3 -m benchmarks.run_suite --suite bfcl
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@@ -74,13 +92,19 @@ python3 -m benchmarks.run_suite --suite bfcl
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#### Run by Category
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```bash
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# All coding benchmarks (~51 problems)
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# All coding benchmarks
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python3 -m benchmarks.run_suite --category coding
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# All math benchmarks (~40 problems)
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# All math benchmarks
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python3 -m benchmarks.run_suite --category math
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# All instruction following benchmarks (~17 problems)
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# All knowledge benchmarks (MMLU-Pro, GPQA, MMMLU, HLE)
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python3 -m benchmarks.run_suite --category knowledge
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# All reasoning benchmarks (BigBench-Hard, Tau2)
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python3 -m benchmarks.run_suite --category reasoning
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# All instruction following benchmarks
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python3 -m benchmarks.run_suite --category instruction-following
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```
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@@ -143,42 +167,41 @@ Each suite looks for a JSONL file in the data directory. If the file isn't found
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| SWE-Bench | `swe_bench.jsonl` | `{"instance_id", "problem_statement", "setup_code", "test_cmd"}` |
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| Aider | `aider.jsonl` | `{"id", "instruction", "setup_code", "test_code"}` |
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| LiveCodeBench | `livecodebench.jsonl` | `{"id", "title", "description", "test_cases", "function_name"}` |
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| Codeforces | `codeforces.jsonl` | `{"id", "rating", "title", "problem", "test_cases"}` |
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| MATH | `math.jsonl` | `{"id", "problem", "answer", "subject", "level"}` |
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| GSM8K | `gsm8k.jsonl` | `{"id", "question", "answer"}` |
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| AIME | `aime.jsonl` | `{"id", "problem", "answer"}` |
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| AIME | `aime.jsonl` or `aime_2026.jsonl` | `{"id", "problem", "answer"}` |
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| IFEval | `ifeval.jsonl` | `{"id", "instruction", "checks"}` |
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| BFCL | `bfcl.jsonl` | `{"id", "instruction", "expected_function", "setup_code", "test_code"}` |
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| MMLU-Pro | `mmlu_pro.jsonl` | `{"id", "subject", "question", "choices", "answer"}` |
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| GPQA-Diamond | `gpqa.jsonl` | `{"id", "subject", "question", "choices", "answer"}` |
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| MMMLU | `mmmlu.jsonl` | `{"id", "language", "subject", "question", "choices", "answer"}` |
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| HLE | `hle.jsonl` | `{"id", "subject", "question", "answer", "answer_type"}` |
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| BigBench-Hard | `bigbench_hard.jsonl` | `{"id", "task", "question", "choices", "answer"}` |
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| Tau2 | `tau2.jsonl` | `{"id", "domain", "question", "choices", "answer"}` |
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### Downloading Full Datasets
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```bash
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# HumanEval (from OpenAI)
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wget -O benchmarks/data/humaneval.jsonl \
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https://raw.githubusercontent.com/openai/human-eval/master/data/HumanEval.jsonl.gz
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gunzip benchmarks/data/humaneval.jsonl.gz
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# Download all datasets (builtin + official where available)
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python3 -m benchmarks.download_datasets --all
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# GSM8K (from HuggingFace — requires datasets library)
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pip install datasets
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python3 -c "
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from datasets import load_dataset
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import json
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ds = load_dataset('gsm8k', 'main', split='test')
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with open('benchmarks/data/gsm8k.jsonl', 'w') as f:
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for i, item in enumerate(ds):
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f.write(json.dumps({'id': f'gsm8k-{i}', 'question': item['question'], 'answer': item['answer'].split('####')[-1].strip()}) + '\n')
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"
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# Download builtin-only (no network, uses embedded problems)
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python3 -m benchmarks.download_datasets --all --builtin-only
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# MATH (from HuggingFace)
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python3 -c "
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from datasets import load_dataset
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import json
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ds = load_dataset('hendrycks/competition_math', split='test')
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with open('benchmarks/data/math.jsonl', 'w') as f:
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for i, item in enumerate(ds):
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f.write(json.dumps({'id': f'math-{i}', 'problem': item['problem'], 'answer': item['solution'], 'subject': item['type'], 'level': item['level']}) + '\n')
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"
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# Download specific suites
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python3 -m benchmarks.download_datasets --suite humaneval --suite mmlu-pro --suite gpqa-diamond
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# Force re-download
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python3 -m benchmarks.download_datasets --all --force
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```
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**Datasets with HuggingFace downloaders:**
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HumanEval, GSM8K, MBPP, MATH, MMLU-Pro, GPQA-Diamond, BigBench-Hard, MMMLU, HLE
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**Builtin-only datasets (no HuggingFace source):**
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SWE-Bench, Aider, LiveCodeBench, AIME, IFEval, BFCL, Tau2, Codeforces
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---
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## Local Task Benchmarks
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@@ -221,16 +244,25 @@ python3 -m benchmarks.run_suite --suite mbpp # Coding: basic Pytho
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python3 -m benchmarks.run_suite --suite swe-bench # Coding: GitHub issues
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python3 -m benchmarks.run_suite --suite aider # Coding: code editing
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python3 -m benchmarks.run_suite --suite livecodebench # Coding: competitive programming
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python3 -m benchmarks.run_suite --suite codeforces # Coding: competitive + ELO
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python3 -m benchmarks.run_suite --suite math # Math: competition math
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python3 -m benchmarks.run_suite --suite gsm8k # Math: grade school
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python3 -m benchmarks.run_suite --suite aime # Math: AIME competition
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python3 -m benchmarks.run_suite --suite mmlu-pro # Knowledge: 14 subjects, 10 choices
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python3 -m benchmarks.run_suite --suite gpqa-diamond # Knowledge: graduate science
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python3 -m benchmarks.run_suite --suite mmmlu # Knowledge: multilingual MMLU
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python3 -m benchmarks.run_suite --suite hle # Knowledge: Humanity's Last Exam
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python3 -m benchmarks.run_suite --suite bigbench-hard # Reasoning: BIG-Bench Hard
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python3 -m benchmarks.run_suite --suite tau2 # Reasoning: tool-augmented
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python3 -m benchmarks.run_suite --suite ifeval # Instruction: format following
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python3 -m benchmarks.run_suite --suite bfcl # Instruction: function calling
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# Category runs
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python3 -m benchmarks.run_suite --category coding # All coding (~51 problems)
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python3 -m benchmarks.run_suite --category math # All math (~40 problems)
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python3 -m benchmarks.run_suite --category instruction-following # All IF (~17 problems)
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python3 -m benchmarks.run_suite --category coding # All coding
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python3 -m benchmarks.run_suite --category math # All math
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python3 -m benchmarks.run_suite --category knowledge # MMLU-Pro, GPQA, MMMLU, HLE
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python3 -m benchmarks.run_suite --category reasoning # BigBench, Tau2
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python3 -m benchmarks.run_suite --category instruction-following # IFEval, BFCL
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# Full run
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python3 -m benchmarks.run_suite --all # All suites (~108 problems)
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@@ -311,6 +343,7 @@ benchmarks/
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├── __init__.py
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├── run.py # Local task benchmark runner
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├── run_suite.py # Standard evaluation suite runner (CLI)
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├── download_datasets.py # Dataset downloader (HuggingFace + builtins)
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├── README.md # This file
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├── data/ # Dataset files (JSONL) — not committed
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│ └── .gitkeep
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@@ -325,9 +358,16 @@ benchmarks/
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├── swe_bench.py # SWE-Bench benchmark
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├── aider.py # Aider benchmark
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├── livecodebench.py # LiveCodeBench benchmark
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├── codeforces.py # Codeforces (ELO scoring)
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├── math_bench.py # MATH benchmark
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├── gsm8k.py # GSM8K benchmark
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├── aime.py # AIME benchmark
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├── mmlu_pro.py # MMLU-Pro (10-choice QA)
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├── gpqa.py # GPQA Diamond (science)
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├── mmmlu.py # MMMLU (multilingual)
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├── hle.py # HLE (Humanity's Last Exam)
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├── bigbench.py # BigBench Extra Hard
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├── tau2.py # Tau2 (tool-augmented)
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├── ifeval.py # IFEval benchmark
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└── bfcl.py # BFCL benchmark
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```
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@@ -18,13 +18,20 @@ from typing import Any, Callable
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from benchmarks.suites.aider import _BUILTIN_PROBLEMS as _AIDER_BUILTINS
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from benchmarks.suites.aime import _BUILTIN_PROBLEMS as _AIME_BUILTINS
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from benchmarks.suites.bfcl import _BUILTIN_PROBLEMS as _BFCL_BUILTINS
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from benchmarks.suites.bigbench import _BUILTIN_PROBLEMS as _BIGBENCH_BUILTINS
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from benchmarks.suites.codeforces import _BUILTIN_PROBLEMS as _CODEFORCES_BUILTINS
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from benchmarks.suites.gpqa import _BUILTIN_PROBLEMS as _GPQA_BUILTINS
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from benchmarks.suites.gsm8k import _BUILTIN_PROBLEMS as _GSM8K_BUILTINS
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from benchmarks.suites.hle import _BUILTIN_PROBLEMS as _HLE_BUILTINS
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from benchmarks.suites.humaneval import _BUILTIN_PROBLEMS as _HUMANEVAL_BUILTINS
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from benchmarks.suites.ifeval import _BUILTIN_PROBLEMS as _IFEVAL_BUILTINS
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from benchmarks.suites.livecodebench import _BUILTIN_PROBLEMS as _LIVECODEBENCH_BUILTINS
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from benchmarks.suites.math_bench import _BUILTIN_PROBLEMS as _MATH_BUILTINS
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from benchmarks.suites.mbpp import _BUILTIN_PROBLEMS as _MBPP_BUILTINS
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from benchmarks.suites.mmmlu import _BUILTIN_PROBLEMS as _MMMLU_BUILTINS
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from benchmarks.suites.mmlu_pro import _BUILTIN_PROBLEMS as _MMLU_PRO_BUILTINS
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from benchmarks.suites.swe_bench import _BUILTIN_PROBLEMS as _SWE_BUILTINS
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from benchmarks.suites.tau2 import _BUILTIN_PROBLEMS as _TAU2_BUILTINS
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HF_DATASET_VIEWER_BASE = "https://datasets-server.huggingface.co"
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@@ -254,6 +261,157 @@ def _download_math(
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return DownloadResult("math", count, str(output_path), "official")
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def _download_mmlu_pro(
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output_path: Path,
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*,
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timeout: float,
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json_fetcher: JsonFetcher = fetch_json,
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) -> DownloadResult:
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rows = _fetch_hf_rows(
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"TIGER-Lab/MMLU-Pro",
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config_preference=("default",),
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split_preference=("test", "validation"),
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json_fetcher=json_fetcher,
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timeout=timeout,
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)
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letters = "ABCDEFGHIJ"
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normalized = [
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{
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"id": f"mmlu-pro-{index + 1:04d}",
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"subject": row.get("category", row.get("subject", "unknown")),
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"question": row.get("question", ""),
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"choices": row.get("options", row.get("choices", [])),
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"answer": letters[row["answer"]] if isinstance(row.get("answer"), int) else str(row.get("answer", "")),
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}
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for index, row in enumerate(rows)
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]
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count = _write_jsonl(output_path, normalized)
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return DownloadResult("mmlu-pro", count, str(output_path), "official")
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def _download_gpqa(
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output_path: Path,
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*,
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timeout: float,
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json_fetcher: JsonFetcher = fetch_json,
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) -> DownloadResult:
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rows = _fetch_hf_rows(
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"Idavidrein/gpqa",
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config_preference=("gpqa_diamond",),
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split_preference=("train",),
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json_fetcher=json_fetcher,
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timeout=timeout,
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)
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normalized = []
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for index, row in enumerate(rows):
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choices = [
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row.get("Correct Answer", ""),
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row.get("Incorrect Answer 1", ""),
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row.get("Incorrect Answer 2", ""),
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row.get("Incorrect Answer 3", ""),
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]
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normalized.append({
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"id": f"gpqa-{index + 1:04d}",
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"subject": row.get("Subdomain", row.get("domain", "science")),
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"question": row.get("Question", ""),
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"choices": choices,
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"answer": "A", # Correct answer is always first; shuffle at eval time if needed
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})
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count = _write_jsonl(output_path, normalized)
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return DownloadResult("gpqa-diamond", count, str(output_path), "official")
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def _download_bigbench_hard(
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output_path: Path,
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*,
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timeout: float,
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json_fetcher: JsonFetcher = fetch_json,
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) -> DownloadResult:
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rows = _fetch_hf_rows(
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"maveriq/bigbenchhard",
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config_preference=("default",),
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split_preference=("train",),
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json_fetcher=json_fetcher,
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timeout=timeout,
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)
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letters = "ABCDEFGHIJ"
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normalized = []
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for index, row in enumerate(rows):
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choices = row.get("choices", row.get("multiple_choice_targets", []))
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answer = row.get("answer", row.get("target", ""))
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if isinstance(answer, int) and answer < len(letters):
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answer = letters[answer]
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normalized.append({
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"id": f"bbh-{index + 1:04d}",
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"task": row.get("task", row.get("subject", "unknown")),
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"question": row.get("input", row.get("question", "")),
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"choices": choices,
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"answer": str(answer),
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})
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count = _write_jsonl(output_path, normalized)
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return DownloadResult("bigbench-hard", count, str(output_path), "official")
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def _download_mmmlu(
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output_path: Path,
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*,
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timeout: float,
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json_fetcher: JsonFetcher = fetch_json,
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) -> DownloadResult:
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rows = _fetch_hf_rows(
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"openai/MMMLU",
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config_preference=("default",),
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split_preference=("test", "validation"),
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json_fetcher=json_fetcher,
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timeout=timeout,
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)
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letters = "ABCD"
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normalized = [
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{
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"id": f"mmmlu-{index + 1:04d}",
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"language": row.get("language", "en"),
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"subject": row.get("subject", "unknown"),
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"question": row.get("question", ""),
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"choices": row.get("choices", row.get("options", [])),
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"answer": letters[row["answer"]] if isinstance(row.get("answer"), int) else str(row.get("answer", "")),
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}
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for index, row in enumerate(rows)
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]
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count = _write_jsonl(output_path, normalized)
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return DownloadResult("mmmlu", count, str(output_path), "official")
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def _download_hle(
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output_path: Path,
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*,
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timeout: float,
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json_fetcher: JsonFetcher = fetch_json,
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) -> DownloadResult:
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rows = _fetch_hf_rows(
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"cais/hle",
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config_preference=("default",),
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split_preference=("test", "validation", "train"),
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json_fetcher=json_fetcher,
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timeout=timeout,
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)
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normalized = []
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for index, row in enumerate(rows):
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entry: dict[str, Any] = {
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"id": f"hle-{index + 1:04d}",
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"subject": row.get("category", row.get("subject", "general")),
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"question": row.get("question", ""),
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"answer": str(row.get("answer", "")),
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}
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if row.get("choices") or row.get("options"):
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entry["answer_type"] = "multiple_choice"
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entry["choices"] = row.get("choices", row.get("options", []))
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else:
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entry["answer_type"] = "exact"
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normalized.append(entry)
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count = _write_jsonl(output_path, normalized)
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return DownloadResult("hle", count, str(output_path), "official")
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def _export_builtin(output_path: Path, suite: str, rows: list[dict[str, Any]], *, source: str = "builtin", note: str = "") -> DownloadResult:
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count = _write_jsonl(output_path, rows)
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return DownloadResult(suite, count, str(output_path), source, note)
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@@ -271,6 +429,13 @@ def _builtin_rows(suite: str) -> list[dict[str, Any]]:
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"aime": list(_AIME_BUILTINS),
|
||||
"ifeval": list(_IFEVAL_BUILTINS),
|
||||
"bfcl": list(_BFCL_BUILTINS),
|
||||
"mmlu-pro": list(_MMLU_PRO_BUILTINS),
|
||||
"gpqa-diamond": list(_GPQA_BUILTINS),
|
||||
"bigbench-hard": list(_BIGBENCH_BUILTINS),
|
||||
"mmmlu": list(_MMMLU_BUILTINS),
|
||||
"hle": list(_HLE_BUILTINS),
|
||||
"tau2": list(_TAU2_BUILTINS),
|
||||
"codeforces": list(_CODEFORCES_BUILTINS),
|
||||
}
|
||||
return list(mapping[suite])
|
||||
|
||||
@@ -284,19 +449,33 @@ def prepare_suite(
|
||||
official_only: bool,
|
||||
timeout: float,
|
||||
) -> DownloadResult:
|
||||
output_path = data_dir / f"{suite}.jsonl"
|
||||
output_map = {
|
||||
"mmlu-pro": "mmlu_pro.jsonl",
|
||||
"gpqa-diamond": "gpqa.jsonl",
|
||||
"bigbench-hard": "bigbench_hard.jsonl",
|
||||
"mmmlu": "mmmlu.jsonl",
|
||||
"hle": "hle.jsonl",
|
||||
"tau2": "tau2.jsonl",
|
||||
"codeforces": "codeforces.jsonl",
|
||||
}
|
||||
output_path = data_dir / output_map.get(suite, f"{suite}.jsonl")
|
||||
if output_path.exists() and not force:
|
||||
lines = [line for line in output_path.read_text(encoding="utf-8").splitlines() if line.strip()]
|
||||
return DownloadResult(suite, len(lines), str(output_path), "existing")
|
||||
|
||||
data_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
builtin_only_suites = {"swe-bench", "aider", "livecodebench", "aime", "ifeval", "bfcl"}
|
||||
builtin_only_suites = {"swe-bench", "aider", "livecodebench", "aime", "ifeval", "bfcl", "tau2", "codeforces"}
|
||||
official_downloaders = {
|
||||
"humaneval": _download_humaneval,
|
||||
"gsm8k": _download_gsm8k,
|
||||
"mbpp": _download_mbpp,
|
||||
"math": _download_math,
|
||||
"mmlu-pro": _download_mmlu_pro,
|
||||
"gpqa-diamond": _download_gpqa,
|
||||
"bigbench-hard": _download_bigbench_hard,
|
||||
"mmmlu": _download_mmmlu,
|
||||
"hle": _download_hle,
|
||||
}
|
||||
|
||||
if suite in builtin_only_suites or builtin_only:
|
||||
@@ -358,6 +537,13 @@ def main() -> None:
|
||||
"aime",
|
||||
"ifeval",
|
||||
"bfcl",
|
||||
"mmlu-pro",
|
||||
"gpqa-diamond",
|
||||
"bigbench-hard",
|
||||
"mmmlu",
|
||||
"hle",
|
||||
"tau2",
|
||||
"codeforces",
|
||||
]
|
||||
|
||||
if args.list:
|
||||
|
||||
+24
-4
@@ -55,6 +55,13 @@ from benchmarks.suites.gsm8k import GSM8KBenchmark
|
||||
from benchmarks.suites.aime import AIMEBenchmark
|
||||
from benchmarks.suites.ifeval import IFEvalBenchmark
|
||||
from benchmarks.suites.bfcl import BFCLBenchmark
|
||||
from benchmarks.suites.mmlu_pro import MMLUProBenchmark
|
||||
from benchmarks.suites.gpqa import GPQABenchmark
|
||||
from benchmarks.suites.bigbench import BigBenchHardBenchmark
|
||||
from benchmarks.suites.mmmlu import MMMMLUBenchmark
|
||||
from benchmarks.suites.hle import HLEBenchmark
|
||||
from benchmarks.suites.tau2 import Tau2Benchmark
|
||||
from benchmarks.suites.codeforces import CodeforcesBenchmark
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
@@ -72,12 +79,21 @@ SUITE_REGISTRY: dict[str, type[BenchmarkSuite]] = {
|
||||
"aime": AIMEBenchmark,
|
||||
"ifeval": IFEvalBenchmark,
|
||||
"bfcl": BFCLBenchmark,
|
||||
"mmlu-pro": MMLUProBenchmark,
|
||||
"gpqa-diamond": GPQABenchmark,
|
||||
"bigbench-hard": BigBenchHardBenchmark,
|
||||
"mmmlu": MMMMLUBenchmark,
|
||||
"hle": HLEBenchmark,
|
||||
"tau2": Tau2Benchmark,
|
||||
"codeforces": CodeforcesBenchmark,
|
||||
}
|
||||
|
||||
CATEGORY_MAP: dict[str, list[str]] = {
|
||||
"coding": ["humaneval", "mbpp", "swe-bench", "aider", "livecodebench"],
|
||||
"coding": ["humaneval", "mbpp", "swe-bench", "aider", "livecodebench", "codeforces"],
|
||||
"math": ["math", "gsm8k", "aime"],
|
||||
"instruction-following": ["ifeval", "bfcl"],
|
||||
"knowledge": ["mmlu-pro", "gpqa-diamond", "mmmlu", "hle"],
|
||||
"reasoning": ["bigbench-hard", "tau2"],
|
||||
}
|
||||
|
||||
|
||||
@@ -175,8 +191,8 @@ def main() -> None:
|
||||
" python3 -m benchmarks.run_suite --all -o results.json\n"
|
||||
),
|
||||
)
|
||||
parser.add_argument("--suite", choices=list(SUITE_REGISTRY.keys()),
|
||||
help="Run a specific benchmark suite")
|
||||
parser.add_argument("--suite", action="append", default=[],
|
||||
help="Run a specific benchmark suite (can be repeated)")
|
||||
parser.add_argument("--category", choices=list(CATEGORY_MAP.keys()),
|
||||
help="Run all suites in a category")
|
||||
parser.add_argument("--all", action="store_true",
|
||||
@@ -221,7 +237,11 @@ def main() -> None:
|
||||
elif args.category:
|
||||
suite_names = CATEGORY_MAP.get(args.category, [])
|
||||
elif args.suite:
|
||||
suite_names = [args.suite]
|
||||
for s in args.suite:
|
||||
if s not in SUITE_REGISTRY:
|
||||
print(f"Error: unknown suite '{s}'. Available: {', '.join(SUITE_REGISTRY)}")
|
||||
sys.exit(1)
|
||||
suite_names = list(args.suite)
|
||||
else:
|
||||
parser.print_help()
|
||||
print("\nError: specify --suite, --category, or --all")
|
||||
|
||||
+14
-12
@@ -84,20 +84,22 @@ class AIMEBenchmark(BenchmarkSuite):
|
||||
category = "math"
|
||||
|
||||
def load_dataset(self) -> list[dict[str, Any]]:
|
||||
jsonl_path = Path(self.data_dir) / "aime.jsonl"
|
||||
if jsonl_path.exists():
|
||||
problems: list[dict[str, Any]] = []
|
||||
with open(jsonl_path) as fh:
|
||||
for line in fh:
|
||||
line = line.strip()
|
||||
if line:
|
||||
problems.append(json.loads(line))
|
||||
if self.verbose:
|
||||
print(f" Loaded {len(problems)} problems from {jsonl_path}")
|
||||
return problems
|
||||
# Prefer aime_2026.jsonl for AIME 2026 specific problems
|
||||
for fname in ("aime_2026.jsonl", "aime.jsonl"):
|
||||
jsonl_path = Path(self.data_dir) / fname
|
||||
if jsonl_path.exists():
|
||||
problems: list[dict[str, Any]] = []
|
||||
with open(jsonl_path) as fh:
|
||||
for line in fh:
|
||||
line = line.strip()
|
||||
if line:
|
||||
problems.append(json.loads(line))
|
||||
if self.verbose:
|
||||
print(f" Loaded {len(problems)} problems from {jsonl_path}")
|
||||
return problems
|
||||
|
||||
if self.verbose:
|
||||
print(f" {jsonl_path} not found — using built-in 10-problem subset")
|
||||
print(" No AIME data file found — using built-in 10-problem subset")
|
||||
return list(_BUILTIN_PROBLEMS)
|
||||
|
||||
def build_prompt(self, problem: dict[str, Any]) -> str:
|
||||
|
||||
@@ -0,0 +1,176 @@
|
||||
"""
|
||||
BIG-Bench Extra Hard benchmark suite.
|
||||
|
||||
BIG-Bench Extra Hard (BBH) is a subset of the most challenging tasks from
|
||||
the BIG-Bench benchmark that language models have historically struggled with.
|
||||
Tasks include logical reasoning, causal judgment, date understanding, etc.
|
||||
|
||||
Reference: https://huggingface.co/datasets/maveriq/bigbenchhard
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
from .base import BenchmarkResult, BenchmarkSuite
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Built-in mini dataset (10 representative BIG-Bench Hard problems)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
_BUILTIN_PROBLEMS: list[dict[str, Any]] = [
|
||||
{
|
||||
"id": "bbh-001",
|
||||
"task": "logical_deduction_three_objects",
|
||||
"question": "The following paragraphs each describe a set of three objects arranged in a fixed order. The statements are logically consistent within each paragraph.\n\nOn a shelf, there are three books: a red book, a green book, and a blue book. The blue book is to the left of the green book. The red book is the second from the left.\n\nWhich book is the leftmost?",
|
||||
"choices": ["The red book", "The green book", "The blue book"],
|
||||
"answer": "C",
|
||||
},
|
||||
{
|
||||
"id": "bbh-002",
|
||||
"task": "causal_judgement",
|
||||
"question": "How would a typical person answer each of the following questions about causation?\n\nBilly and Suzy each throw a rock at a bottle. Suzy's rock arrives first and shatters the bottle. Billy's rock arrives at where the bottle was a moment later.\n\nDid Billy cause the bottle to shatter?",
|
||||
"choices": ["Yes", "No"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "bbh-003",
|
||||
"task": "date_understanding",
|
||||
"question": "Today is Christmas Eve of 1937. What is the date tomorrow in MM/DD/YYYY?",
|
||||
"choices": ["12/11/1937", "12/25/1937", "01/04/1938", "12/04/1937", "12/25/2006", "09/25/1937"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "bbh-004",
|
||||
"task": "disambiguation_qa",
|
||||
"question": "In the following sentences, explain the antecedent of the pronoun (which thing the pronoun refers to), or state that it is ambiguous.\n\nSentence: The scientist thanked the librarian because she was helpful.\n\nWho was helpful?",
|
||||
"choices": ["The scientist", "The librarian", "Ambiguous"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "bbh-005",
|
||||
"task": "navigate",
|
||||
"question": "If you follow these instructions, do you return to the starting point?\n\nTake 1 step east. Take 2 steps north. Take 1 step west. Take 2 steps south.",
|
||||
"choices": ["Yes", "No"],
|
||||
"answer": "A",
|
||||
},
|
||||
{
|
||||
"id": "bbh-006",
|
||||
"task": "penguins_in_a_table",
|
||||
"question": "Here is a table where the first line is a header and each subsequent line is a penguin:\n\nname, age, height (cm), weight (kg)\nLouis, 7, 50, 11\nBernard, 5, 80, 13\nVincent, 9, 60, 11\nGwen, 8, 70, 15\n\nWhat is the name of the tallest penguin?",
|
||||
"choices": ["Louis", "Bernard", "Vincent", "Gwen"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "bbh-007",
|
||||
"task": "sports_understanding",
|
||||
"question": "Is the following sentence plausible?\n\n\"Lebron James scored a touchdown.\"",
|
||||
"choices": ["plausible", "not plausible"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "bbh-008",
|
||||
"task": "boolean_expressions",
|
||||
"question": "Evaluate the result of a random Boolean expression.\n\nnot ( ( not not True ) ) is",
|
||||
"choices": ["True", "False"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "bbh-009",
|
||||
"task": "web_of_lies",
|
||||
"question": "Evaluate a random Boolean function expressed as a word problem.\n\nQuestion: Fidel tells the truth. Jerry says Fidel tells the truth. Vina says Jerry lies. Millicent says Vina tells the truth. Does Millicent tell the truth?",
|
||||
"choices": ["Yes", "No"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "bbh-010",
|
||||
"task": "tracking_shuffled_objects_three_objects",
|
||||
"question": "Alice, Bob, and Claire are playing a game. At the start of the game, they are each holding a ball: Alice has a red ball, Bob has a blue ball, and Claire has a green ball.\n\nAs the game progresses, pairs of players trade balls. First, Alice and Bob swap balls. Then, Bob and Claire swap balls.\n\nAt the end of the game, what ball does Bob have?",
|
||||
"choices": ["red ball", "blue ball", "green ball"],
|
||||
"answer": "C",
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
class BigBenchHardBenchmark(BenchmarkSuite):
|
||||
"""BIG-Bench Extra Hard: Challenging reasoning tasks from BIG-Bench."""
|
||||
|
||||
name = "BigBench-Extra-Hard"
|
||||
description = "Challenging multi-step reasoning tasks from BIG-Bench"
|
||||
category = "reasoning"
|
||||
|
||||
def load_dataset(self) -> list[dict[str, Any]]:
|
||||
jsonl_path = Path(self.data_dir) / "bigbench_hard.jsonl"
|
||||
if jsonl_path.exists():
|
||||
problems: list[dict[str, Any]] = []
|
||||
with open(jsonl_path) as fh:
|
||||
for line in fh:
|
||||
line = line.strip()
|
||||
if line:
|
||||
problems.append(json.loads(line))
|
||||
if self.verbose:
|
||||
print(f" Loaded {len(problems)} problems from {jsonl_path}")
|
||||
return problems
|
||||
|
||||
if self.verbose:
|
||||
print(f" {jsonl_path} not found — using built-in 10-problem subset")
|
||||
return list(_BUILTIN_PROBLEMS)
|
||||
|
||||
def build_prompt(self, problem: dict[str, Any]) -> str:
|
||||
question = problem["question"]
|
||||
choices = problem.get("choices", [])
|
||||
letters = "ABCDEFGHIJ"
|
||||
choices_text = "\n".join(
|
||||
f" {letters[i]}. {c}" for i, c in enumerate(choices)
|
||||
)
|
||||
return (
|
||||
f"Answer the following reasoning question.\n\n"
|
||||
f"Question: {question}\n\n"
|
||||
f"Choices:\n{choices_text}\n\n"
|
||||
f"Think step by step, then write ONLY the letter of the correct answer "
|
||||
f"to a file called answer.txt — no explanation, just the single letter."
|
||||
)
|
||||
|
||||
def recover_output_files(
|
||||
self,
|
||||
problem: dict[str, Any],
|
||||
workspace: str,
|
||||
agent_output: str,
|
||||
metadata: dict[str, Any],
|
||||
) -> None:
|
||||
del problem
|
||||
answer_path = Path(workspace) / "answer.txt"
|
||||
if answer_path.exists():
|
||||
return
|
||||
match = re.search(r"\b([A-J])\b", agent_output)
|
||||
if match:
|
||||
answer_path.write_text(match.group(1) + "\n", encoding="utf-8")
|
||||
metadata["recovered_answer_from_output"] = True
|
||||
|
||||
def evaluate(self, problem: dict[str, Any], workspace: str) -> BenchmarkResult:
|
||||
pid = problem.get("id", "unknown")
|
||||
expected = str(problem["answer"]).strip().upper()
|
||||
answer_file = os.path.join(workspace, "answer.txt")
|
||||
|
||||
if not os.path.exists(answer_file):
|
||||
return BenchmarkResult(
|
||||
problem_id=pid, passed=False, expected=expected,
|
||||
error="answer.txt not found",
|
||||
)
|
||||
|
||||
with open(answer_file) as fh:
|
||||
actual_raw = fh.read().strip()
|
||||
|
||||
match = re.search(r"\b([A-J])\b", actual_raw.upper())
|
||||
actual = match.group(1) if match else actual_raw.strip().upper()
|
||||
|
||||
passed = actual == expected
|
||||
return BenchmarkResult(
|
||||
problem_id=pid, passed=passed,
|
||||
expected=expected, actual=actual_raw,
|
||||
error="" if passed else f"expected={expected}, got={actual_raw}",
|
||||
)
|
||||
@@ -0,0 +1,275 @@
|
||||
"""
|
||||
Codeforces benchmark suite.
|
||||
|
||||
Evaluates competitive programming ability using Codeforces-style problems.
|
||||
Unlike pass/fail suites, this suite computes an ELO-like rating based on
|
||||
problem difficulty and correctness.
|
||||
|
||||
Reference: https://codeforces.com/
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import math
|
||||
import os
|
||||
import re
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
from .base import BenchmarkResult, BenchmarkSuite, SuiteReport
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Built-in mini dataset (10 Codeforces-style problems with difficulty ratings)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
_BUILTIN_PROBLEMS: list[dict[str, Any]] = [
|
||||
{
|
||||
"id": "cf-800-001",
|
||||
"rating": 800,
|
||||
"title": "Watermelon",
|
||||
"problem": "Pete and Billy have a watermelon weighing w kilograms. They want to divide it into two parts, each weighing an even number of kilograms. Determine if this is possible.\n\nInput: A single integer w (1 ≤ w ≤ 100)\nOutput: Print YES if possible, NO otherwise.",
|
||||
"test_cases": [
|
||||
{"input": "8", "expected_output": "YES"},
|
||||
{"input": "3", "expected_output": "NO"},
|
||||
{"input": "1", "expected_output": "NO"},
|
||||
{"input": "2", "expected_output": "NO"},
|
||||
{"input": "4", "expected_output": "YES"},
|
||||
],
|
||||
},
|
||||
{
|
||||
"id": "cf-800-002",
|
||||
"rating": 800,
|
||||
"title": "Way Too Long Words",
|
||||
"problem": "Abbreviate words longer than 10 characters. For such words, output the first letter, number of middle characters, and last letter.\n\nInput: First line is n (1 ≤ n ≤ 100). Next n lines each contain a word.\nOutput: For each word, output the abbreviation or the word itself if length ≤ 10.",
|
||||
"test_cases": [
|
||||
{"input": "4\nword\nlocalization\ninternationalization\npneumonoultramicroscopicsilicovolcanoconiosis", "expected_output": "word\nl10n\ni18n\np43s"},
|
||||
{"input": "1\nabcdefghij", "expected_output": "abcdefghij"},
|
||||
],
|
||||
},
|
||||
{
|
||||
"id": "cf-1000-001",
|
||||
"rating": 1000,
|
||||
"title": "Nearly Lucky Number",
|
||||
"problem": "A number is nearly lucky if the count of digits 4 and 7 in it is itself a lucky number (composed only of 4s and 7s). Given n, determine if it is nearly lucky.\n\nInput: A single integer n (1 ≤ n ≤ 10^18)\nOutput: YES or NO",
|
||||
"test_cases": [
|
||||
{"input": "40047", "expected_output": "NO"},
|
||||
{"input": "7747774", "expected_output": "YES"},
|
||||
{"input": "1000000000000000000", "expected_output": "NO"},
|
||||
],
|
||||
},
|
||||
{
|
||||
"id": "cf-1200-001",
|
||||
"rating": 1200,
|
||||
"title": "Beautiful Matrix",
|
||||
"problem": "You have a 5×5 matrix with exactly one 1 and rest 0s. In one move, you can swap any two adjacent rows or columns. Find the minimum number of moves to place the 1 in the center (row 3, col 3).\n\nInput: 5 lines each with 5 space-separated integers.\nOutput: Minimum number of moves.",
|
||||
"test_cases": [
|
||||
{"input": "0 0 0 0 0\n0 0 0 0 1\n0 0 0 0 0\n0 0 0 0 0\n0 0 0 0 0", "expected_output": "3"},
|
||||
{"input": "0 0 0 0 0\n0 0 0 0 0\n0 0 1 0 0\n0 0 0 0 0\n0 0 0 0 0", "expected_output": "0"},
|
||||
],
|
||||
},
|
||||
{
|
||||
"id": "cf-1400-001",
|
||||
"rating": 1400,
|
||||
"title": "Kefa and Park",
|
||||
"problem": "Kefa wants to walk from the root (node 1) to any leaf in a tree. Along the path, there are cats at some nodes. Find the number of leaves reachable such that the path doesn't contain more than m consecutive cats.\n\nInput: First line: n m (n nodes, max m consecutive cats). Second line: n integers (0/1) for each node. Next n-1 lines: edges.\nOutput: Number of valid leaves.\n\nFor the built-in test, n=7 m=1, cats=[1,1,0,0,1,0,1], edges: 1-2, 1-3, 2-4, 2-5, 3-6, 3-7",
|
||||
"test_cases": [
|
||||
{"input": "7 1\n1 1 0 0 1 0 1\n1 2\n1 3\n2 4\n2 5\n3 6\n3 7", "expected_output": "2"},
|
||||
],
|
||||
},
|
||||
{
|
||||
"id": "cf-1600-001",
|
||||
"rating": 1600,
|
||||
"title": "Divisibility by Eight",
|
||||
"problem": "Given a number (as a string of up to 100 digits), determine if you can delete some digits to get a non-empty number divisible by 8. Leading zeros are allowed in the result.\n\nInput: A string of digits.\nOutput: YES and the resulting number, or NO.",
|
||||
"test_cases": [
|
||||
{"input": "3121", "expected_output": "YES\n312"},
|
||||
{"input": "123456789", "expected_output": "YES\n8"},
|
||||
{"input": "3", "expected_output": "NO"},
|
||||
],
|
||||
},
|
||||
{
|
||||
"id": "cf-1800-001",
|
||||
"rating": 1800,
|
||||
"title": "Array Partition",
|
||||
"problem": "Given an array a of n integers, determine if you can partition it into three non-empty contiguous parts such that max(part1) = min(part2) = max(part3).\n\nInput: First line n. Second line: a1...an.\nOutput: YES and the lengths of three parts, or NO.",
|
||||
"test_cases": [
|
||||
{"input": "5\n3 1 5 3 1", "expected_output": "YES"},
|
||||
{"input": "3\n1 2 3", "expected_output": "NO"},
|
||||
],
|
||||
},
|
||||
{
|
||||
"id": "cf-2000-001",
|
||||
"rating": 2000,
|
||||
"title": "Count Binary Strings",
|
||||
"problem": "Count the number of binary strings of length n where no two adjacent characters are both '1'. Output the answer modulo 10^9 + 7.\n\nInput: A single integer n (1 ≤ n ≤ 10^6)\nOutput: The count mod 10^9+7.",
|
||||
"test_cases": [
|
||||
{"input": "1", "expected_output": "2"},
|
||||
{"input": "2", "expected_output": "3"},
|
||||
{"input": "3", "expected_output": "5"},
|
||||
{"input": "10", "expected_output": "144"},
|
||||
],
|
||||
},
|
||||
{
|
||||
"id": "cf-2200-001",
|
||||
"rating": 2200,
|
||||
"title": "Xor Sequences",
|
||||
"problem": "Given an array of n distinct non-negative integers, find the number of pairs (i,j) where i < j such that a[i] XOR a[j] has an even number of set bits.\n\nInput: First line n. Second line: a1...an.\nOutput: Number of such pairs.",
|
||||
"test_cases": [
|
||||
{"input": "3\n1 2 3", "expected_output": "1"},
|
||||
{"input": "4\n0 1 2 3", "expected_output": "2"},
|
||||
],
|
||||
},
|
||||
{
|
||||
"id": "cf-2500-001",
|
||||
"rating": 2500,
|
||||
"title": "Segment Tree Query",
|
||||
"problem": "Given an array of n integers, answer q queries. Each query gives l, r and asks for the sum of elements from index l to r (1-indexed). Implement this efficiently.\n\nInput: First line: n q. Second line: a1...an. Next q lines: l r.\nOutput: q lines with answers.",
|
||||
"test_cases": [
|
||||
{"input": "5 3\n1 2 3 4 5\n1 3\n2 4\n1 5", "expected_output": "6\n9\n15"},
|
||||
],
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
def _compute_elo(results: list[BenchmarkResult], problems: list[dict[str, Any]]) -> float:
|
||||
"""Compute an approximate ELO rating from problem results and difficulty ratings."""
|
||||
if not results:
|
||||
return 0.0
|
||||
|
||||
# Build a mapping from problem_id to rating
|
||||
rating_map = {str(p.get("id", "")): p.get("rating", 1200) for p in problems}
|
||||
|
||||
# Start with base rating 800
|
||||
elo = 800.0
|
||||
k_factor = 40.0
|
||||
|
||||
for result in results:
|
||||
problem_rating = rating_map.get(result.problem_id, 1200)
|
||||
# Expected score based on ELO difference
|
||||
expected = 1.0 / (1.0 + math.pow(10, (problem_rating - elo) / 400.0))
|
||||
actual_score = 1.0 if result.passed else 0.0
|
||||
elo += k_factor * (actual_score - expected)
|
||||
|
||||
return round(max(0, elo))
|
||||
|
||||
|
||||
class CodeforcesBenchmark(BenchmarkSuite):
|
||||
"""Codeforces: Competitive programming with ELO-based scoring."""
|
||||
|
||||
name = "Codeforces"
|
||||
description = "Competitive programming problems with ELO rating estimation"
|
||||
category = "coding"
|
||||
|
||||
def load_dataset(self) -> list[dict[str, Any]]:
|
||||
jsonl_path = Path(self.data_dir) / "codeforces.jsonl"
|
||||
if jsonl_path.exists():
|
||||
problems: list[dict[str, Any]] = []
|
||||
with open(jsonl_path) as fh:
|
||||
for line in fh:
|
||||
line = line.strip()
|
||||
if line:
|
||||
problems.append(json.loads(line))
|
||||
if self.verbose:
|
||||
print(f" Loaded {len(problems)} problems from {jsonl_path}")
|
||||
return problems
|
||||
|
||||
if self.verbose:
|
||||
print(f" {jsonl_path} not found — using built-in 10-problem subset")
|
||||
return list(_BUILTIN_PROBLEMS)
|
||||
|
||||
def build_prompt(self, problem: dict[str, Any]) -> str:
|
||||
title = problem.get("title", "Problem")
|
||||
text = problem["problem"]
|
||||
test_cases = problem.get("test_cases", [])
|
||||
|
||||
examples = ""
|
||||
for i, tc in enumerate(test_cases[:2], 1):
|
||||
examples += f"\nExample {i}:\n Input: {tc['input']}\n Output: {tc['expected_output']}\n"
|
||||
|
||||
return (
|
||||
f"Solve the following competitive programming problem.\n\n"
|
||||
f"Title: {title}\n\n"
|
||||
f"Problem:\n{text}\n"
|
||||
f"{examples}\n"
|
||||
f"Write a Python solution in a file called solution.py that reads from "
|
||||
f"stdin and writes to stdout."
|
||||
)
|
||||
|
||||
def setup_workspace(self, problem: dict[str, Any], workspace: str) -> None:
|
||||
# Write test cases to workspace for evaluation
|
||||
test_cases = problem.get("test_cases", [])
|
||||
for i, tc in enumerate(test_cases):
|
||||
input_file = os.path.join(workspace, f"test_input_{i}.txt")
|
||||
expected_file = os.path.join(workspace, f"test_expected_{i}.txt")
|
||||
with open(input_file, "w") as f:
|
||||
f.write(tc["input"] + "\n")
|
||||
with open(expected_file, "w") as f:
|
||||
f.write(tc["expected_output"].strip() + "\n")
|
||||
|
||||
def evaluate(self, problem: dict[str, Any], workspace: str) -> BenchmarkResult:
|
||||
pid = problem.get("id", "unknown")
|
||||
rating = problem.get("rating", 1200)
|
||||
solution_file = os.path.join(workspace, "solution.py")
|
||||
|
||||
if not os.path.exists(solution_file):
|
||||
return BenchmarkResult(
|
||||
problem_id=pid, passed=False,
|
||||
error="solution.py not found",
|
||||
metadata={"rating": rating},
|
||||
)
|
||||
|
||||
test_cases = problem.get("test_cases", [])
|
||||
all_passed = True
|
||||
errors = []
|
||||
|
||||
for i, tc in enumerate(test_cases):
|
||||
input_data = tc["input"]
|
||||
expected = tc["expected_output"].strip()
|
||||
|
||||
rc, output = self._run_shell(
|
||||
f"echo {repr(input_data)} | python3 solution.py",
|
||||
cwd=workspace,
|
||||
timeout=10.0,
|
||||
)
|
||||
|
||||
actual = output.strip()
|
||||
if rc != 0:
|
||||
all_passed = False
|
||||
errors.append(f"test {i}: runtime error: {output[:200]}")
|
||||
elif actual != expected:
|
||||
# Check if the output matches any valid answer
|
||||
# (for problems with YES/NO + additional output)
|
||||
if expected.startswith("YES") and actual.startswith("YES"):
|
||||
pass # Accept if at least "YES" is correct
|
||||
elif expected.startswith("NO") and actual.startswith("NO"):
|
||||
pass
|
||||
else:
|
||||
all_passed = False
|
||||
errors.append(f"test {i}: expected={expected!r}, got={actual!r}")
|
||||
|
||||
return BenchmarkResult(
|
||||
problem_id=pid, passed=all_passed,
|
||||
expected=f"all {len(test_cases)} tests",
|
||||
actual=f"{'all passed' if all_passed else '; '.join(errors)}",
|
||||
error="" if all_passed else "; ".join(errors),
|
||||
metadata={"rating": rating},
|
||||
)
|
||||
|
||||
def run_all(self) -> SuiteReport:
|
||||
"""Override to add ELO computation to the report."""
|
||||
report = super().run_all()
|
||||
problems = self.load_dataset()
|
||||
if self.limit is not None:
|
||||
problems = problems[: self.limit]
|
||||
elo = _compute_elo(report.results, problems)
|
||||
print(f" Estimated Codeforces ELO: {elo}")
|
||||
report.results.append(
|
||||
BenchmarkResult(
|
||||
problem_id="_elo_rating",
|
||||
passed=True,
|
||||
actual=str(elo),
|
||||
metadata={"elo_rating": elo},
|
||||
)
|
||||
)
|
||||
return report
|
||||
@@ -0,0 +1,176 @@
|
||||
"""
|
||||
GPQA Diamond benchmark suite.
|
||||
|
||||
GPQA (Graduate-Level Google-Proof Question Answering) Diamond is a subset
|
||||
of extremely difficult questions written by domain experts in biology,
|
||||
physics, and chemistry. The "Diamond" subset is the hardest tier.
|
||||
|
||||
Reference: https://huggingface.co/datasets/Idavidrein/gpqa
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
from .base import BenchmarkResult, BenchmarkSuite
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Built-in mini dataset (10 representative GPQA Diamond problems)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
_BUILTIN_PROBLEMS: list[dict[str, Any]] = [
|
||||
{
|
||||
"id": "gpqa-001",
|
||||
"subject": "physics",
|
||||
"question": "A particle of mass m is confined to a one-dimensional box of length L. What is the energy difference between the first excited state and the ground state?",
|
||||
"choices": ["3π²ℏ²/(2mL²)", "π²ℏ²/(2mL²)", "4π²ℏ²/(2mL²)", "2π²ℏ²/(mL²)"],
|
||||
"answer": "A",
|
||||
},
|
||||
{
|
||||
"id": "gpqa-002",
|
||||
"subject": "chemistry",
|
||||
"question": "Which of the following molecules has the highest bond dissociation energy?",
|
||||
"choices": ["N₂", "O₂", "F₂", "CO"],
|
||||
"answer": "D",
|
||||
},
|
||||
{
|
||||
"id": "gpqa-003",
|
||||
"subject": "biology",
|
||||
"question": "In the lac operon, which component acts as the inducer that causes the repressor to release from the operator?",
|
||||
"choices": ["Lactose", "Allolactose", "Glucose", "cAMP"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "gpqa-004",
|
||||
"subject": "physics",
|
||||
"question": "In quantum electrodynamics, what is the leading-order correction to the electron g-factor (anomalous magnetic moment)?",
|
||||
"choices": ["α/(2π)", "α/π", "α²/(2π)", "2α/π"],
|
||||
"answer": "A",
|
||||
},
|
||||
{
|
||||
"id": "gpqa-005",
|
||||
"subject": "chemistry",
|
||||
"question": "What is the primary product when 2-methylpropene undergoes hydroboration-oxidation?",
|
||||
"choices": ["2-methylpropan-2-ol", "2-methylpropan-1-ol", "2-methylpropanal", "isobutylene oxide"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "gpqa-006",
|
||||
"subject": "biology",
|
||||
"question": "Which of the following enzymes is responsible for adding a 5' cap to mRNA in eukaryotes?",
|
||||
"choices": ["RNA polymerase II", "Guanylyltransferase", "Poly(A) polymerase", "RNA triphosphatase alone"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "gpqa-007",
|
||||
"subject": "physics",
|
||||
"question": "In general relativity, the Schwarzschild radius of a black hole with mass M is given by:",
|
||||
"choices": ["2GM/c²", "GM/c²", "GM²/c", "2GM²/c²"],
|
||||
"answer": "A",
|
||||
},
|
||||
{
|
||||
"id": "gpqa-008",
|
||||
"subject": "chemistry",
|
||||
"question": "Which of the following is the correct order of acidity for the hydrogen halides in aqueous solution?",
|
||||
"choices": ["HF > HCl > HBr > HI", "HI > HBr > HCl > HF", "HCl > HBr > HI > HF", "HF > HI > HBr > HCl"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "gpqa-009",
|
||||
"subject": "biology",
|
||||
"question": "What is the primary function of topoisomerase II during DNA replication?",
|
||||
"choices": ["Unwinding the double helix", "Relieving positive supercoiling ahead of the replication fork", "Joining Okazaki fragments", "Proofreading newly synthesized DNA"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "gpqa-010",
|
||||
"subject": "physics",
|
||||
"question": "In the Standard Model, the Higgs mechanism gives mass to which fundamental particles?",
|
||||
"choices": ["Only quarks", "Only W and Z bosons", "W bosons, Z bosons, and all fermions", "All particles including photons and gluons"],
|
||||
"answer": "C",
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
class GPQABenchmark(BenchmarkSuite):
|
||||
"""GPQA Diamond: Graduate-level science QA (physics, chemistry, biology)."""
|
||||
|
||||
name = "GPQA-Diamond"
|
||||
description = "Graduate-level science multiple-choice (diamond difficulty)"
|
||||
category = "knowledge"
|
||||
|
||||
def load_dataset(self) -> list[dict[str, Any]]:
|
||||
jsonl_path = Path(self.data_dir) / "gpqa.jsonl"
|
||||
if jsonl_path.exists():
|
||||
problems: list[dict[str, Any]] = []
|
||||
with open(jsonl_path) as fh:
|
||||
for line in fh:
|
||||
line = line.strip()
|
||||
if line:
|
||||
problems.append(json.loads(line))
|
||||
if self.verbose:
|
||||
print(f" Loaded {len(problems)} problems from {jsonl_path}")
|
||||
return problems
|
||||
|
||||
if self.verbose:
|
||||
print(f" {jsonl_path} not found — using built-in 10-problem subset")
|
||||
return list(_BUILTIN_PROBLEMS)
|
||||
|
||||
def build_prompt(self, problem: dict[str, Any]) -> str:
|
||||
question = problem["question"]
|
||||
choices = problem.get("choices", [])
|
||||
letters = "ABCD"
|
||||
choices_text = "\n".join(
|
||||
f" {letters[i]}. {c}" for i, c in enumerate(choices)
|
||||
)
|
||||
return (
|
||||
f"Answer the following graduate-level science question.\n\n"
|
||||
f"Question: {question}\n\n"
|
||||
f"Choices:\n{choices_text}\n\n"
|
||||
f"Think carefully and write ONLY the letter of the correct answer (A–D) "
|
||||
f"to a file called answer.txt — no explanation, just the single letter."
|
||||
)
|
||||
|
||||
def recover_output_files(
|
||||
self,
|
||||
problem: dict[str, Any],
|
||||
workspace: str,
|
||||
agent_output: str,
|
||||
metadata: dict[str, Any],
|
||||
) -> None:
|
||||
del problem
|
||||
answer_path = Path(workspace) / "answer.txt"
|
||||
if answer_path.exists():
|
||||
return
|
||||
match = re.search(r"\b([A-D])\b", agent_output)
|
||||
if match:
|
||||
answer_path.write_text(match.group(1) + "\n", encoding="utf-8")
|
||||
metadata["recovered_answer_from_output"] = True
|
||||
|
||||
def evaluate(self, problem: dict[str, Any], workspace: str) -> BenchmarkResult:
|
||||
pid = problem.get("id", "unknown")
|
||||
expected = str(problem["answer"]).strip().upper()
|
||||
answer_file = os.path.join(workspace, "answer.txt")
|
||||
|
||||
if not os.path.exists(answer_file):
|
||||
return BenchmarkResult(
|
||||
problem_id=pid, passed=False, expected=expected,
|
||||
error="answer.txt not found",
|
||||
)
|
||||
|
||||
with open(answer_file) as fh:
|
||||
actual_raw = fh.read().strip()
|
||||
|
||||
match = re.search(r"\b([A-D])\b", actual_raw.upper())
|
||||
actual = match.group(1) if match else actual_raw.strip().upper()
|
||||
|
||||
passed = actual == expected
|
||||
return BenchmarkResult(
|
||||
problem_id=pid, passed=passed,
|
||||
expected=expected, actual=actual_raw,
|
||||
error="" if passed else f"expected={expected}, got={actual_raw}",
|
||||
)
|
||||
@@ -0,0 +1,199 @@
|
||||
"""
|
||||
HLE (Humanity's Last Exam) benchmark suite.
|
||||
|
||||
HLE is an extremely challenging benchmark designed to test the limits of
|
||||
AI capabilities. It contains questions from diverse domains that are
|
||||
intended to be among the hardest questions answerable by humans.
|
||||
|
||||
Reference: https://huggingface.co/datasets/cais/hle
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
from .base import BenchmarkResult, BenchmarkSuite
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Built-in mini dataset (10 representative HLE-style problems)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
_BUILTIN_PROBLEMS: list[dict[str, Any]] = [
|
||||
{
|
||||
"id": "hle-001",
|
||||
"subject": "mathematics",
|
||||
"question": "What is the smallest positive integer n such that n! ends with exactly 100 trailing zeros?",
|
||||
"answer_type": "exact",
|
||||
"answer": "405",
|
||||
},
|
||||
{
|
||||
"id": "hle-002",
|
||||
"subject": "physics",
|
||||
"question": "In natural units (ℏ = c = 1), the fine-structure constant α ≈ 1/137. What is the approximate ratio of the electromagnetic force to the gravitational force between two protons?",
|
||||
"answer_type": "multiple_choice",
|
||||
"choices": ["10^36", "10^24", "10^42", "10^18"],
|
||||
"answer": "A",
|
||||
},
|
||||
{
|
||||
"id": "hle-003",
|
||||
"subject": "computer_science",
|
||||
"question": "What is the time complexity of the best known algorithm for matrix multiplication as of 2024?",
|
||||
"answer_type": "multiple_choice",
|
||||
"choices": ["O(n^2.371552)", "O(n^2.5)", "O(n^3)", "O(n^2 log n)"],
|
||||
"answer": "A",
|
||||
},
|
||||
{
|
||||
"id": "hle-004",
|
||||
"subject": "mathematics",
|
||||
"question": "How many groups of order 16 are there up to isomorphism?",
|
||||
"answer_type": "exact",
|
||||
"answer": "14",
|
||||
},
|
||||
{
|
||||
"id": "hle-005",
|
||||
"subject": "chemistry",
|
||||
"question": "What is the maximum number of stereoisomers possible for a molecule with 3 chiral centers and no meso forms?",
|
||||
"answer_type": "exact",
|
||||
"answer": "8",
|
||||
},
|
||||
{
|
||||
"id": "hle-006",
|
||||
"subject": "biology",
|
||||
"question": "The human genome contains approximately how many protein-coding genes?",
|
||||
"answer_type": "multiple_choice",
|
||||
"choices": ["~5,000", "~20,000", "~100,000", "~500,000"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "hle-007",
|
||||
"subject": "mathematics",
|
||||
"question": "What is the value of the Ramanujan sum c_5(3)?",
|
||||
"answer_type": "exact",
|
||||
"answer": "1",
|
||||
},
|
||||
{
|
||||
"id": "hle-008",
|
||||
"subject": "physics",
|
||||
"question": "What is the spin of the Higgs boson?",
|
||||
"answer_type": "exact",
|
||||
"answer": "0",
|
||||
},
|
||||
{
|
||||
"id": "hle-009",
|
||||
"subject": "computer_science",
|
||||
"question": "In computational complexity theory, which of the following containment relationships is known to be strict?",
|
||||
"answer_type": "multiple_choice",
|
||||
"choices": ["P ⊂ NP", "AC0 ⊂ NC1", "NP ⊂ PSPACE", "L ⊂ NL"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "hle-010",
|
||||
"subject": "mathematics",
|
||||
"question": "What is the chromatic number of the Petersen graph?",
|
||||
"answer_type": "exact",
|
||||
"answer": "3",
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
class HLEBenchmark(BenchmarkSuite):
|
||||
"""HLE: Humanity's Last Exam — extremely challenging expert questions."""
|
||||
|
||||
name = "HLE"
|
||||
description = "Extremely challenging expert-level questions (Humanity's Last Exam)"
|
||||
category = "knowledge"
|
||||
|
||||
def load_dataset(self) -> list[dict[str, Any]]:
|
||||
jsonl_path = Path(self.data_dir) / "hle.jsonl"
|
||||
if jsonl_path.exists():
|
||||
problems: list[dict[str, Any]] = []
|
||||
with open(jsonl_path) as fh:
|
||||
for line in fh:
|
||||
line = line.strip()
|
||||
if line:
|
||||
problems.append(json.loads(line))
|
||||
if self.verbose:
|
||||
print(f" Loaded {len(problems)} problems from {jsonl_path}")
|
||||
return problems
|
||||
|
||||
if self.verbose:
|
||||
print(f" {jsonl_path} not found — using built-in 10-problem subset")
|
||||
return list(_BUILTIN_PROBLEMS)
|
||||
|
||||
def build_prompt(self, problem: dict[str, Any]) -> str:
|
||||
question = problem["question"]
|
||||
answer_type = problem.get("answer_type", "exact")
|
||||
|
||||
if answer_type == "multiple_choice" and "choices" in problem:
|
||||
choices = problem["choices"]
|
||||
letters = "ABCD"
|
||||
choices_text = "\n".join(
|
||||
f" {letters[i]}. {c}" for i, c in enumerate(choices)
|
||||
)
|
||||
return (
|
||||
f"Answer the following extremely challenging question.\n\n"
|
||||
f"Question: {question}\n\n"
|
||||
f"Choices:\n{choices_text}\n\n"
|
||||
f"Think very carefully. Write ONLY the letter of the correct answer "
|
||||
f"to a file called answer.txt — no explanation, just the single letter."
|
||||
)
|
||||
else:
|
||||
return (
|
||||
f"Answer the following extremely challenging question.\n\n"
|
||||
f"Question: {question}\n\n"
|
||||
f"Think very carefully. Write ONLY the final answer "
|
||||
f"to a file called answer.txt — no explanation, just the answer."
|
||||
)
|
||||
|
||||
def recover_output_files(
|
||||
self,
|
||||
problem: dict[str, Any],
|
||||
workspace: str,
|
||||
agent_output: str,
|
||||
metadata: dict[str, Any],
|
||||
) -> None:
|
||||
del problem
|
||||
answer_path = Path(workspace) / "answer.txt"
|
||||
if answer_path.exists():
|
||||
return
|
||||
lines = agent_output.strip().splitlines()
|
||||
if lines:
|
||||
answer_path.write_text(lines[-1].strip() + "\n", encoding="utf-8")
|
||||
metadata["recovered_answer_from_output"] = True
|
||||
|
||||
def evaluate(self, problem: dict[str, Any], workspace: str) -> BenchmarkResult:
|
||||
pid = problem.get("id", "unknown")
|
||||
expected = str(problem["answer"]).strip()
|
||||
answer_type = problem.get("answer_type", "exact")
|
||||
answer_file = os.path.join(workspace, "answer.txt")
|
||||
|
||||
if not os.path.exists(answer_file):
|
||||
return BenchmarkResult(
|
||||
problem_id=pid, passed=False, expected=expected,
|
||||
error="answer.txt not found",
|
||||
)
|
||||
|
||||
with open(answer_file) as fh:
|
||||
actual_raw = fh.read().strip()
|
||||
|
||||
if answer_type == "multiple_choice":
|
||||
match = re.search(r"\b([A-D])\b", actual_raw.upper())
|
||||
actual = match.group(1) if match else actual_raw.strip().upper()
|
||||
passed = actual == expected.upper()
|
||||
else:
|
||||
# Exact match — normalize numbers
|
||||
actual = actual_raw.strip()
|
||||
try:
|
||||
passed = float(actual) == float(expected)
|
||||
except (ValueError, TypeError):
|
||||
passed = actual.lower() == expected.lower()
|
||||
|
||||
return BenchmarkResult(
|
||||
problem_id=pid, passed=passed,
|
||||
expected=expected, actual=actual_raw,
|
||||
error="" if passed else f"expected={expected}, got={actual_raw}",
|
||||
)
|
||||
@@ -0,0 +1,176 @@
|
||||
"""
|
||||
MMLU-Pro benchmark suite.
|
||||
|
||||
MMLU-Pro (Massive Multitask Language Understanding - Professional) is an
|
||||
enhanced version of MMLU with harder questions, 10 answer choices (A–J),
|
||||
and chain-of-thought reasoning requirements across 14 subjects.
|
||||
|
||||
Reference: https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
from .base import BenchmarkResult, BenchmarkSuite
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Built-in mini dataset (10 representative MMLU-Pro problems)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
_BUILTIN_PROBLEMS: list[dict[str, Any]] = [
|
||||
{
|
||||
"id": "mmlu-pro-001",
|
||||
"subject": "computer_science",
|
||||
"question": "Which of the following best describes the time complexity of binary search on a sorted array of n elements?",
|
||||
"choices": ["O(1)", "O(log n)", "O(n)", "O(n log n)", "O(n^2)", "O(2^n)", "O(n!)", "O(sqrt(n))", "O(n^3)", "O(log log n)"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "mmlu-pro-002",
|
||||
"subject": "physics",
|
||||
"question": "A 2 kg block is placed on a frictionless inclined plane that makes a 30° angle with the horizontal. What is the acceleration of the block down the incline? (g = 9.8 m/s²)",
|
||||
"choices": ["2.45 m/s²", "4.9 m/s²", "6.93 m/s²", "9.8 m/s²", "3.27 m/s²", "8.49 m/s²", "1.63 m/s²", "5.66 m/s²", "7.35 m/s²", "0.98 m/s²"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "mmlu-pro-003",
|
||||
"subject": "chemistry",
|
||||
"question": "What is the hybridization of the central atom in SF6?",
|
||||
"choices": ["sp", "sp2", "sp3", "sp3d", "sp3d2", "dsp3", "d2sp3", "sp2d", "sp3d3", "none of the above"],
|
||||
"answer": "E",
|
||||
},
|
||||
{
|
||||
"id": "mmlu-pro-004",
|
||||
"subject": "mathematics",
|
||||
"question": "What is the derivative of f(x) = x³ · ln(x)?",
|
||||
"choices": ["3x² · ln(x)", "x² + 3x² · ln(x)", "3x² · ln(x) + x²", "x³/x + 3x²", "3x² · ln(x) + x³", "x² · (3ln(x) + 1)", "3x · ln(x) + x²", "x³ · (1/x) + 3x² · ln(x)", "3x² / ln(x)", "ln(x) + 3x²"],
|
||||
"answer": "F",
|
||||
},
|
||||
{
|
||||
"id": "mmlu-pro-005",
|
||||
"subject": "biology",
|
||||
"question": "Which of the following is NOT a characteristic of the adaptive immune response?",
|
||||
"choices": ["Specificity", "Memory", "Rapid initial response", "Diversity", "Self/non-self discrimination", "Clonal expansion", "Tolerance", "MHC restriction", "Somatic hypermutation", "Pattern recognition"],
|
||||
"answer": "C",
|
||||
},
|
||||
{
|
||||
"id": "mmlu-pro-006",
|
||||
"subject": "history",
|
||||
"question": "The Treaty of Westphalia (1648) is most significant because it:",
|
||||
"choices": ["Ended the Hundred Years War", "Established the principle of state sovereignty", "Created the United Nations", "Divided Africa among European powers", "Ended World War I", "Established the Holy Roman Empire", "Created NATO", "Ended the Napoleonic Wars", "Established the European Union", "Ended the Crusades"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "mmlu-pro-007",
|
||||
"subject": "economics",
|
||||
"question": "In a perfectly competitive market in long-run equilibrium, which of the following is true?",
|
||||
"choices": ["Price equals minimum ATC", "Firms earn positive economic profit", "Price exceeds marginal cost", "Firms produce at maximum ATC", "Barriers to entry exist", "Price equals maximum AVC", "Firms are price makers", "Marginal cost exceeds price", "Economic profit is negative", "Supply is perfectly inelastic"],
|
||||
"answer": "A",
|
||||
},
|
||||
{
|
||||
"id": "mmlu-pro-008",
|
||||
"subject": "philosophy",
|
||||
"question": "According to Kant's categorical imperative, an action is morally permissible if and only if:",
|
||||
"choices": ["It maximizes overall happiness", "It can be universalized without contradiction", "It promotes the greatest good for the greatest number", "God commands it", "It follows natural law", "It leads to virtuous character", "It is agreed upon by rational contractors", "It satisfies one's desires", "It is consistent with social norms", "It minimizes suffering"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "mmlu-pro-009",
|
||||
"subject": "law",
|
||||
"question": "Under the U.S. Constitution, which amendment protects against unreasonable searches and seizures?",
|
||||
"choices": ["First Amendment", "Second Amendment", "Third Amendment", "Fourth Amendment", "Fifth Amendment", "Sixth Amendment", "Seventh Amendment", "Eighth Amendment", "Ninth Amendment", "Tenth Amendment"],
|
||||
"answer": "D",
|
||||
},
|
||||
{
|
||||
"id": "mmlu-pro-010",
|
||||
"subject": "psychology",
|
||||
"question": "In Piaget's theory of cognitive development, the stage in which children develop the ability to think abstractly and reason hypothetically is called the:",
|
||||
"choices": ["Sensorimotor stage", "Preoperational stage", "Concrete operational stage", "Formal operational stage", "Postformal stage", "Latency stage", "Genital stage", "Identity vs. role confusion", "Autonomy vs. shame", "Initiative vs. guilt"],
|
||||
"answer": "D",
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
class MMLUProBenchmark(BenchmarkSuite):
|
||||
"""MMLU-Pro: Enhanced multitask language understanding with 10-choice questions."""
|
||||
|
||||
name = "MMLU-Pro"
|
||||
description = "Professional-level multiple-choice QA across 14 subjects (10 choices)"
|
||||
category = "knowledge"
|
||||
|
||||
def load_dataset(self) -> list[dict[str, Any]]:
|
||||
jsonl_path = Path(self.data_dir) / "mmlu_pro.jsonl"
|
||||
if jsonl_path.exists():
|
||||
problems: list[dict[str, Any]] = []
|
||||
with open(jsonl_path) as fh:
|
||||
for line in fh:
|
||||
line = line.strip()
|
||||
if line:
|
||||
problems.append(json.loads(line))
|
||||
if self.verbose:
|
||||
print(f" Loaded {len(problems)} problems from {jsonl_path}")
|
||||
return problems
|
||||
|
||||
if self.verbose:
|
||||
print(f" {jsonl_path} not found — using built-in 10-problem subset")
|
||||
return list(_BUILTIN_PROBLEMS)
|
||||
|
||||
def build_prompt(self, problem: dict[str, Any]) -> str:
|
||||
question = problem["question"]
|
||||
choices = problem.get("choices", [])
|
||||
letters = "ABCDEFGHIJ"
|
||||
choices_text = "\n".join(
|
||||
f" {letters[i]}. {c}" for i, c in enumerate(choices)
|
||||
)
|
||||
return (
|
||||
f"Answer the following multiple-choice question.\n\n"
|
||||
f"Question: {question}\n\n"
|
||||
f"Choices:\n{choices_text}\n\n"
|
||||
f"Write ONLY the letter of the correct answer (A–J) to a file called answer.txt — "
|
||||
f"no explanation, just the single letter."
|
||||
)
|
||||
|
||||
def recover_output_files(
|
||||
self,
|
||||
problem: dict[str, Any],
|
||||
workspace: str,
|
||||
agent_output: str,
|
||||
metadata: dict[str, Any],
|
||||
) -> None:
|
||||
del problem
|
||||
answer_path = Path(workspace) / "answer.txt"
|
||||
if answer_path.exists():
|
||||
return
|
||||
match = re.search(r"\b([A-J])\b", agent_output)
|
||||
if match:
|
||||
answer_path.write_text(match.group(1) + "\n", encoding="utf-8")
|
||||
metadata["recovered_answer_from_output"] = True
|
||||
|
||||
def evaluate(self, problem: dict[str, Any], workspace: str) -> BenchmarkResult:
|
||||
pid = problem.get("id", "unknown")
|
||||
expected = str(problem["answer"]).strip().upper()
|
||||
answer_file = os.path.join(workspace, "answer.txt")
|
||||
|
||||
if not os.path.exists(answer_file):
|
||||
return BenchmarkResult(
|
||||
problem_id=pid, passed=False, expected=expected,
|
||||
error="answer.txt not found",
|
||||
)
|
||||
|
||||
with open(answer_file) as fh:
|
||||
actual_raw = fh.read().strip()
|
||||
|
||||
match = re.search(r"\b([A-J])\b", actual_raw.upper())
|
||||
actual = match.group(1) if match else actual_raw.strip().upper()
|
||||
|
||||
passed = actual == expected
|
||||
return BenchmarkResult(
|
||||
problem_id=pid, passed=passed,
|
||||
expected=expected, actual=actual_raw,
|
||||
error="" if passed else f"expected={expected}, got={actual_raw}",
|
||||
)
|
||||
@@ -0,0 +1,191 @@
|
||||
"""
|
||||
MMMLU (Multilingual Massive Multitask Language Understanding) benchmark suite.
|
||||
|
||||
MMMLU extends MMLU to multiple languages, testing language understanding
|
||||
across diverse cultural and linguistic contexts.
|
||||
|
||||
Reference: https://huggingface.co/datasets/openai/MMMLU
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
from .base import BenchmarkResult, BenchmarkSuite
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Built-in mini dataset (10 representative MMMLU problems — multilingual)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
_BUILTIN_PROBLEMS: list[dict[str, Any]] = [
|
||||
{
|
||||
"id": "mmmlu-001",
|
||||
"language": "en",
|
||||
"subject": "abstract_algebra",
|
||||
"question": "Find the degree of the extension Q(sqrt(2), sqrt(3), sqrt(18)) over Q.",
|
||||
"choices": ["0", "4", "2", "6"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "mmmlu-002",
|
||||
"language": "es",
|
||||
"subject": "anatomy",
|
||||
"question": "¿Cuál de los siguientes músculos es el principal responsable de la abducción del brazo?",
|
||||
"choices": ["Deltoides", "Trapecio", "Pectoral mayor", "Dorsal ancho"],
|
||||
"answer": "A",
|
||||
},
|
||||
{
|
||||
"id": "mmmlu-003",
|
||||
"language": "fr",
|
||||
"subject": "astronomy",
|
||||
"question": "Quelle est la planète la plus proche du Soleil?",
|
||||
"choices": ["Vénus", "Terre", "Mercure", "Mars"],
|
||||
"answer": "C",
|
||||
},
|
||||
{
|
||||
"id": "mmmlu-004",
|
||||
"language": "de",
|
||||
"subject": "business_ethics",
|
||||
"question": "Welcher der folgenden Begriffe beschreibt die Verantwortung eines Unternehmens gegenüber der Gesellschaft am besten?",
|
||||
"choices": ["Corporate Social Responsibility", "Shareholder Theory", "Laissez-faire", "Monopol"],
|
||||
"answer": "A",
|
||||
},
|
||||
{
|
||||
"id": "mmmlu-005",
|
||||
"language": "zh",
|
||||
"subject": "computer_science",
|
||||
"question": "在计算机科学中，二叉搜索树的平均时间复杂度是多少？",
|
||||
"choices": ["O(n)", "O(log n)", "O(n²)", "O(1)"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "mmmlu-006",
|
||||
"language": "ja",
|
||||
"subject": "world_history",
|
||||
"question": "フランス革命が始まったのは何年ですか？",
|
||||
"choices": ["1776年", "1789年", "1804年", "1815年"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "mmmlu-007",
|
||||
"language": "pt",
|
||||
"subject": "geography",
|
||||
"question": "Qual é o rio mais longo do mundo?",
|
||||
"choices": ["Amazonas", "Nilo", "Mississipi", "Yangtzé"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "mmmlu-008",
|
||||
"language": "it",
|
||||
"subject": "philosophy",
|
||||
"question": "Chi ha scritto 'La Repubblica'?",
|
||||
"choices": ["Aristotele", "Platone", "Socrate", "Epicuro"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "mmmlu-009",
|
||||
"language": "ko",
|
||||
"subject": "physics",
|
||||
"question": "뉴턴의 제2법칙에서 힘의 공식은?",
|
||||
"choices": ["F = mv", "F = ma", "F = mg", "F = mc²"],
|
||||
"answer": "B",
|
||||
},
|
||||
{
|
||||
"id": "mmmlu-010",
|
||||
"language": "ar",
|
||||
"subject": "chemistry",
|
||||
"question": "ما هو العنصر الأكثر وفرة في القشرة الأرضية؟",
|
||||
"choices": ["الحديد", "الأكسجين", "السيليكون", "الألمنيوم"],
|
||||
"answer": "B",
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
class MMMMLUBenchmark(BenchmarkSuite):
|
||||
"""MMMLU: Multilingual MMLU across diverse languages."""
|
||||
|
||||
name = "MMMLU"
|
||||
description = "Multilingual multiple-choice QA across languages and subjects"
|
||||
category = "knowledge"
|
||||
|
||||
def load_dataset(self) -> list[dict[str, Any]]:
|
||||
jsonl_path = Path(self.data_dir) / "mmmlu.jsonl"
|
||||
if jsonl_path.exists():
|
||||
problems: list[dict[str, Any]] = []
|
||||
with open(jsonl_path) as fh:
|
||||
for line in fh:
|
||||
line = line.strip()
|
||||
if line:
|
||||
problems.append(json.loads(line))
|
||||
if self.verbose:
|
||||
print(f" Loaded {len(problems)} problems from {jsonl_path}")
|
||||
return problems
|
||||
|
||||
if self.verbose:
|
||||
print(f" {jsonl_path} not found — using built-in 10-problem subset")
|
||||
return list(_BUILTIN_PROBLEMS)
|
||||
|
||||
def build_prompt(self, problem: dict[str, Any]) -> str:
|
||||
question = problem["question"]
|
||||
choices = problem.get("choices", [])
|
||||
language = problem.get("language", "en")
|
||||
letters = "ABCD"
|
||||
choices_text = "\n".join(
|
||||
f" {letters[i]}. {c}" for i, c in enumerate(choices)
|
||||
)
|
||||
|
||||
lang_instruction = ""
|
||||
if language != "en":
|
||||
lang_instruction = f"The question is in {language}. "
|
||||
|
||||
return (
|
||||
f"Answer the following multiple-choice question. {lang_instruction}\n\n"
|
||||
f"Question: {question}\n\n"
|
||||
f"Choices:\n{choices_text}\n\n"
|
||||
f"Write ONLY the letter of the correct answer (A–D) to a file called answer.txt — "
|
||||
f"no explanation, just the single letter."
|
||||
)
|
||||
|
||||
def recover_output_files(
|
||||
self,
|
||||
problem: dict[str, Any],
|
||||
workspace: str,
|
||||
agent_output: str,
|
||||
metadata: dict[str, Any],
|
||||
) -> None:
|
||||
del problem
|
||||
answer_path = Path(workspace) / "answer.txt"
|
||||
if answer_path.exists():
|
||||
return
|
||||
match = re.search(r"\b([A-D])\b", agent_output)
|
||||
if match:
|
||||
answer_path.write_text(match.group(1) + "\n", encoding="utf-8")
|
||||
metadata["recovered_answer_from_output"] = True
|
||||
|
||||
def evaluate(self, problem: dict[str, Any], workspace: str) -> BenchmarkResult:
|
||||
pid = problem.get("id", "unknown")
|
||||
expected = str(problem["answer"]).strip().upper()
|
||||
answer_file = os.path.join(workspace, "answer.txt")
|
||||
|
||||
if not os.path.exists(answer_file):
|
||||
return BenchmarkResult(
|
||||
problem_id=pid, passed=False, expected=expected,
|
||||
error="answer.txt not found",
|
||||
)
|
||||
|
||||
with open(answer_file) as fh:
|
||||
actual_raw = fh.read().strip()
|
||||
|
||||
match = re.search(r"\b([A-D])\b", actual_raw.upper())
|
||||
actual = match.group(1) if match else actual_raw.strip().upper()
|
||||
|
||||
passed = actual == expected
|
||||
return BenchmarkResult(
|
||||
problem_id=pid, passed=passed,
|
||||
expected=expected, actual=actual_raw,
|
||||
error="" if passed else f"expected={expected}, got={actual_raw}",
|
||||
)
|
||||
@@ -0,0 +1,181 @@
|
||||
"""
|
||||
Tau2 benchmark suite.
|
||||
|
||||
Tau2 (TAU-bench v2) evaluates language models on tool-augmented tasks
|
||||
requiring multi-step reasoning with tool use. It measures the ability
|
||||
to plan, select, and chain tool calls to complete complex tasks.
|
||||
|
||||
The benchmark reports an average score across 3 task domains:
|
||||
retail, airline, and finance.
|
||||
|
||||
Reference: https://github.com/sierra-research/tau-bench
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
from .base import BenchmarkResult, BenchmarkSuite
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Built-in mini dataset (10 representative Tau2-style problems)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
_BUILTIN_PROBLEMS: list[dict[str, Any]] = [
|
||||
{
|
||||
"id": "tau2-001",
|
||||
"domain": "retail",
|
||||
"question": "A customer wants to return a laptop purchased 20 days ago. The store policy allows returns within 30 days with receipt. The customer has the receipt. The laptop is in original packaging. What is the correct action?",
|
||||
"choices": ["Process full refund", "Deny return - past return window", "Offer store credit only", "Process exchange only"],
|
||||
"answer": "A",
|
||||
},
|
||||
{
|
||||
"id": "tau2-002",
|
||||
"domain": "airline",
|
||||
"question": "A passenger has a connecting flight with a 45-minute layover. Their first flight is delayed by 30 minutes. The minimum connection time at the hub airport is 60 minutes. What should the agent recommend?",
|
||||
"choices": ["Rebook on next available flight", "Keep current booking, passenger will make it", "Cancel entire itinerary", "Upgrade to first class on delayed flight"],
|
||||
"answer": "A",
|
||||
},
|
||||
{
|
||||
"id": "tau2-003",
|
||||
"domain": "finance",
|
||||
"question": "A client wants to transfer $50,000 between accounts. The daily transfer limit is $25,000. The client has proper identification. What is the correct procedure?",
|
||||
"choices": ["Split into two $25,000 transfers over two days", "Override limit and process single transfer", "Deny the transfer entirely", "Process as a wire transfer instead"],
|
||||
"answer": "A",
|
||||
},
|
||||
{
|
||||
"id": "tau2-004",
|
||||
"domain": "retail",
|
||||
"question": "An item shows a price of $29.99 on the shelf but rings up as $34.99 at checkout. Store policy states the customer gets the lower price if there's a discrepancy. The shelf tag is verified to be for this item. What action should be taken?",
|
||||
"choices": ["Honor the shelf price of $29.99", "Charge the register price of $34.99", "Split the difference", "Give the item for free"],
|
||||
"answer": "A",
|
||||
},
|
||||
{
|
||||
"id": "tau2-005",
|
||||
"domain": "airline",
|
||||
"question": "A passenger with a non-refundable ticket wants to change their travel date. The fare difference is +$150 and there is a $75 change fee. The passenger is a Gold status member (change fees waived). What is the total additional cost?",
|
||||
"choices": ["$150", "$225", "$75", "$0"],
|
||||
"answer": "A",
|
||||
},
|
||||
{
|
||||
"id": "tau2-006",
|
||||
"domain": "finance",
|
||||
"question": "A customer's account shows 3 pending transactions totaling $500, but their available balance is $400. One pending transaction of $200 is a pre-authorization from a gas station from 5 days ago. What should the agent do?",
|
||||
"choices": ["Release the gas station pre-authorization as it exceeds the standard hold period", "Decline all pending transactions", "Suggest the customer deposit more funds", "Freeze the account for suspicious activity"],
|
||||
"answer": "A",
|
||||
},
|
||||
{
|
||||
"id": "tau2-007",
|
||||
"domain": "retail",
|
||||
"question": "A customer purchased an appliance with a 2-year manufacturer warranty 18 months ago. The appliance has stopped working due to a manufacturing defect. The customer does not have an extended warranty. What should the agent do?",
|
||||
"choices": ["Process warranty claim with manufacturer", "Offer store repair at customer's cost", "Deny any assistance", "Offer replacement at discounted price"],
|
||||
"answer": "A",
|
||||
},
|
||||
{
|
||||
"id": "tau2-008",
|
||||
"domain": "airline",
|
||||
"question": "A flight is overbooked by 2 passengers. 3 passengers volunteer to give up their seats. Compensation offered is $400 voucher + next flight (2 hours later). Airline policy requires bumping in reverse check-in order if volunteers insufficient. What should happen?",
|
||||
"choices": ["Accept 2 of the 3 volunteers, compensate them", "Accept all 3 volunteers", "Involuntarily deny 2 passengers boarding", "Cancel the flight"],
|
||||
"answer": "A",
|
||||
},
|
||||
{
|
||||
"id": "tau2-009",
|
||||
"domain": "finance",
|
||||
"question": "A customer reports their credit card stolen. There are 3 unauthorized transactions in the last 24 hours totaling $1,200. Federal regulation limits customer liability for unauthorized transactions reported within 2 business days. What steps should be taken first?",
|
||||
"choices": ["Block the card immediately and initiate fraud investigation", "Wait for the monthly statement to dispute charges", "Transfer balance to new card without blocking old one", "Ask customer to contact merchants directly"],
|
||||
"answer": "A",
|
||||
},
|
||||
{
|
||||
"id": "tau2-010",
|
||||
"domain": "retail",
|
||||
"question": "A customer wants to price match an identical item found cheaper at a competitor. Store policy allows price matching for identical items at local competitors within 14 days of purchase. The customer purchased 10 days ago and has proof of the competitor's current price. The competitor is an online-only store. Store policy explicitly excludes online-only retailers. What should the agent do?",
|
||||
"choices": ["Deny price match - online-only retailers excluded", "Honor the price match anyway", "Offer partial price adjustment", "Contact competitor to verify price"],
|
||||
"answer": "A",
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
class Tau2Benchmark(BenchmarkSuite):
|
||||
"""Tau2: Tool-augmented task evaluation across retail, airline, and finance."""
|
||||
|
||||
name = "Tau2"
|
||||
description = "Tool-augmented multi-step reasoning (retail/airline/finance)"
|
||||
category = "reasoning"
|
||||
|
||||
def load_dataset(self) -> list[dict[str, Any]]:
|
||||
jsonl_path = Path(self.data_dir) / "tau2.jsonl"
|
||||
if jsonl_path.exists():
|
||||
problems: list[dict[str, Any]] = []
|
||||
with open(jsonl_path) as fh:
|
||||
for line in fh:
|
||||
line = line.strip()
|
||||
if line:
|
||||
problems.append(json.loads(line))
|
||||
if self.verbose:
|
||||
print(f" Loaded {len(problems)} problems from {jsonl_path}")
|
||||
return problems
|
||||
|
||||
if self.verbose:
|
||||
print(f" {jsonl_path} not found — using built-in 10-problem subset")
|
||||
return list(_BUILTIN_PROBLEMS)
|
||||
|
||||
def build_prompt(self, problem: dict[str, Any]) -> str:
|
||||
question = problem["question"]
|
||||
choices = problem.get("choices", [])
|
||||
domain = problem.get("domain", "general")
|
||||
letters = "ABCD"
|
||||
choices_text = "\n".join(
|
||||
f" {letters[i]}. {c}" for i, c in enumerate(choices)
|
||||
)
|
||||
return (
|
||||
f"You are a {domain} service agent. Answer the following question "
|
||||
f"about the correct action to take.\n\n"
|
||||
f"Scenario: {question}\n\n"
|
||||
f"Options:\n{choices_text}\n\n"
|
||||
f"Write ONLY the letter of the correct answer (A–D) to a file called answer.txt — "
|
||||
f"no explanation, just the single letter."
|
||||
)
|
||||
|
||||
def recover_output_files(
|
||||
self,
|
||||
problem: dict[str, Any],
|
||||
workspace: str,
|
||||
agent_output: str,
|
||||
metadata: dict[str, Any],
|
||||
) -> None:
|
||||
del problem
|
||||
answer_path = Path(workspace) / "answer.txt"
|
||||
if answer_path.exists():
|
||||
return
|
||||
match = re.search(r"\b([A-D])\b", agent_output)
|
||||
if match:
|
||||
answer_path.write_text(match.group(1) + "\n", encoding="utf-8")
|
||||
metadata["recovered_answer_from_output"] = True
|
||||
|
||||
def evaluate(self, problem: dict[str, Any], workspace: str) -> BenchmarkResult:
|
||||
pid = problem.get("id", "unknown")
|
||||
expected = str(problem["answer"]).strip().upper()
|
||||
answer_file = os.path.join(workspace, "answer.txt")
|
||||
|
||||
if not os.path.exists(answer_file):
|
||||
return BenchmarkResult(
|
||||
problem_id=pid, passed=False, expected=expected,
|
||||
error="answer.txt not found",
|
||||
)
|
||||
|
||||
with open(answer_file) as fh:
|
||||
actual_raw = fh.read().strip()
|
||||
|
||||
match = re.search(r"\b([A-D])\b", actual_raw.upper())
|
||||
actual = match.group(1) if match else actual_raw.strip().upper()
|
||||
|
||||
passed = actual == expected
|
||||
return BenchmarkResult(
|
||||
problem_id=pid, passed=passed,
|
||||
expected=expected, actual=actual_raw,
|
||||
error="" if passed else f"expected={expected}, got={actual_raw}",
|
||||
)
|
||||
Reference in New Issue
Block a user