130 lines
3.5 KiB
Markdown
130 lines
3.5 KiB
Markdown
# GPQA-Diamond
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## Overview
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GPQA (Graduate-Level Google-Proof Q&A) Diamond is a challenging benchmark of 198 multiple-choice questions written by domain experts in biology, physics, and chemistry. The questions are designed to be extremely difficult, requiring PhD-level expertise to answer correctly.
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## Task Description
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- **Task Type**: Expert-Level Multiple-Choice Q&A
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- **Input**: Graduate-level science question with 4 choices
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- **Output**: Single correct answer letter (A, B, C, or D)
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- **Domains**: Biology, Physics, Chemistry
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## Key Features
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- 198 questions written and validated by domain PhD experts
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- Questions are "Google-proof" - cannot be easily looked up
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- Designed to test deep domain knowledge and reasoning
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- Diamond subset represents the highest quality questions
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- Average human expert accuracy ~65%, non-expert ~34%
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## Evaluation Notes
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- Default configuration uses **0-shot** or **5-shot** evaluation
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- Supports Chain-of-Thought (CoT) prompting for improved reasoning
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- Answer choices are randomly shuffled during evaluation
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- Only uses train split (validation set is private)
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- Challenging benchmark for measuring expert-level reasoning
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `gpqa_diamond` |
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| **Dataset ID** | [AI-ModelScope/gpqa_diamond](https://modelscope.cn/datasets/AI-ModelScope/gpqa_diamond/summary) |
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| **Paper** | N/A |
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| **Tags** | `Knowledge`, `MCQ` |
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| **Metrics** | `acc` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `train` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 198 |
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| Prompt Length (Mean) | 841.15 chars |
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| Prompt Length (Min/Max) | 340 / 5845 chars |
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## Sample Example
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**Subset**: `default`
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```json
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{
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"input": [
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{
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"id": "82b448a9",
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"content": "Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of A,B,C,D. Think step by step before answering.\n\nTwo quantum states wi ... [TRUNCATED] ... and 10^-8 sec, respectively. We want to clearly distinguish these two energy levels. Which one of the following options could be their energy difference so that they can be clearly resolved?\n\n\nA) 10^-4 eV\nB) 10^-9 eV\nC) 10^-8 eV\nD) 10^-11 eV"
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}
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],
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"choices": [
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"10^-4 eV",
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"10^-9 eV",
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"10^-8 eV",
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"10^-11 eV"
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],
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"target": "A",
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"id": 0,
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"group_id": 0,
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"subset_key": "",
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"metadata": {
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"correct_answer": "10^-4 eV",
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"incorrect_answers": [
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"10^-11 eV",
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"10^-8 eV\n",
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"10^-9 eV"
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]
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}
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}
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```
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*Note: Some content was truncated for display.*
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## Prompt Template
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**Prompt Template:**
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```text
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Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}. Think step by step before answering.
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{question}
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{choices}
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```
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## Usage
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### Using CLI
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```bash
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evalscope eval \
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--model YOUR_MODEL \
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--api-url OPENAI_API_COMPAT_URL \
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--api-key EMPTY_TOKEN \
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--datasets gpqa_diamond \
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--limit 10 # Remove this line for formal evaluation
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```
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### Using Python
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```python
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from evalscope import run_task
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from evalscope.config import TaskConfig
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task_cfg = TaskConfig(
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model='YOUR_MODEL',
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api_url='OPENAI_API_COMPAT_URL',
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api_key='EMPTY_TOKEN',
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datasets=['gpqa_diamond'],
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limit=10, # Remove this line for formal evaluation
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)
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run_task(task_cfg=task_cfg)
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```
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