GPQA Diamond

Evaluate your Large Language Models (LLMs) on graduate-level scientific reasoning using the GPQA Diamond benchmark. This Action Pack guides you through accessing the dataset and setting up an evaluation pipeline to assess your LLM's deep reasoning capabilities against PhD-level questions.

intermediate1 hour6 steps

The play

Understand the Benchmark
Grasp that GPQA Diamond is a high-stakes, PhD-level science benchmark designed to rigorously test LLM's deep reasoning, not just factual recall.
Locate the Dataset
Find the official GPQA Diamond dataset and associated tools. This typically involves checking the project's GitHub repository or official academic release for download instructions.
Prepare Your LLM
Load the Large Language Model you wish to evaluate. Ensure it's configured for inference and can process complex scientific queries effectively.
Develop Evaluation Script
Write or adapt a Python script that loads the GPQA Diamond questions, feeds them to your LLM, captures its answers, and compares them against the ground truth for scoring.
Execute Benchmark
Run your evaluation script. This process can be resource-intensive and time-consuming depending on your LLM, hardware, and the full dataset size.
Analyze Results
Review the scores and specific question failures to understand your LLM's strengths and weaknesses in scientific reasoning and identify areas for improvement.

Starter code

git clone https://github.com/openai/gpqa.git
cd gpqa
pip install -e .
# Follow the repository's README for dataset download and usage instructions

Source

Paperarxiv.org