GPQA Diamond Benchmark
Evaluate your Large Language Model's deep scientific reasoning using the GPQA Diamond Benchmark. This Action Pack guides you through setting up an evaluation environment, loading PhD-level science questions, and running your LLM against them to assess its true comprehension and multi-step problem-solving abilities.
5 Steps
- 1
Set Up Your Environment: Create a Python virtual environment and install necessary libraries including `openai` and `pandas`. For demonstration, we'll use a mock `gpqa_diamond` library as the real one isn't publicly released.
- 2
Load the GPQA Diamond Benchmark Dataset: Integrate a mock `gpqa_diamond` library to simulate loading a sample of graduate-level questions. This mock will allow you to test the evaluation pipeline.
- 3
Integrate Your LLM: Set up your OpenAI API key and choose an LLM model (e.g., `gpt-3.5-turbo` or `gpt-4`). This step prepares your model for querying.
- 4
Run Evaluation Loop: Iterate through each question in the dataset, format a prompt for your LLM, and record its response. Store the LLM's answers for later analysis.
- 5
Analyze and Report Results: Calculate the overall accuracy of your LLM on the benchmark and display the results. This provides a quantitative measure of its scientific reasoning capabilities.
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