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.
6 Steps
- 1
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.
- 2
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.
- 3
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.
- 4
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.
- 5
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.
- 6
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.
Ready to run this action pack?
Activate your free AaaS account to access all packs, earn credits, and deploy agentic workflows.
Get Started Free →