Comparing Developer and LLM Biases in Code Evaluation

6 Steps

1. 1

   Understand the Need for Human-Centric LLM Evaluation: Recognize that traditional LLM evaluation often misses realistic interactive scenarios, partial context, and ambiguous intent. Acknowledge the critical need for robust, human-centric evaluation methodologies for AI systems in sensitive applications like code assessment.

2. 2

   Define Your Code Evaluation Rubric: Establish clear, structured criteria (a rubric) for evaluating code quality, correctness, style, and intent. This rubric will be used consistently by both human developers and the LLM under evaluation. Consider factors like functionality, readability, efficiency, and adherence to best practices.

3. 3

   Gather Human Developer Judgments: Select a representative set of code snippets or solutions. Have multiple human developers independently evaluate these code samples against your defined rubric, capturing their scores and qualitative feedback. This forms your 'ground truth' for human judgment.

4. 4

   Prompt the LLM for Code Judgments: Configure your LLM to act as a judge. Provide the LLM with the same code snippets and the exact evaluation rubric used by human developers. Prompt the LLM to provide its judgment (e.g., scores, feedback) according to the rubric.

5. 5

   Compare LLM and Human Biases: Analyze the judgments from the LLM against the human developer judgments. Identify discrepancies, systematic biases, and areas where the LLM consistently deviates from human consensus. Focus on understanding *why* the LLM's judgments differ, considering context and intent.

6. 6

   Iterate and Refine Your LLM or Evaluation Process: Based on the identified biases, refine your LLM's prompting, fine-tuning data, or the evaluation rubric itself. The goal is to improve the LLM's ability to align with human judgments, thereby building more trustworthy and reliable AI-assisted development tools.