Large language models used as judges in automated evaluation systems can exhibit inconsistencies, leading to unreliable results. Factors such as sampling temperature, model version drift, prompt ambiguity, and tie-breaking mechanisms contribute to this variability. To mitigate these issues, developers can implement strategies like setting temperature to zero, pinning specific model versions and prompt versions, averaging scores over multiple runs, and quantizing scores to a coarser grid. Additionally, a significant challenge is position bias, where LLM judges tend to favor the first answer presented, which can skew evaluation metrics. Addressing this requires running pairwise comparisons in both presentation orders to measure disagreement and identify the extent of the bias. AI
IMPACT Inconsistent LLM judges and position bias can lead to unreliable model evaluations, necessitating robust methodologies for accurate performance assessment.
RANK_REASON The cluster discusses research findings and methods for improving the reproducibility and fairness of LLM-based evaluation systems.
- Chatbot Arena
- GPT-4
- Neural Information Processing Systems 2023
- Zheng et al. reply
- claude-latest
- GPT-4o
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