LLM Bias Evaluation Framework
What it is
A model-agnostic pipeline for measuring and mitigating four specific biases (verbosity, score inflation, position, and method disagreement) in LLM-as-a-Judge systems. It uses a synthetic peer-review dataset to demonstrate how different evaluation methods (rubric vs. pairwise) can yield conflicting results and provides calibration techniques to improve consistency.
The problem it solves
LLM-as-a-Judge systems are often trusted blindly, but they suffer from systematic biases (like preferring longer answers or the first option) that can invalidate evaluation results.
What you could use it for
- Auditing LLM-based evaluation systems for systematic biases
- Comparing the reliability of rubric-based vs. pairwise comparison methods
- Calibrating LLM judges to reduce score variance and inflation
- Educational demonstration of LLM evaluation pitfalls
Read the source ↗Back to the library →
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