Summary of Judging the Judges: a Systematic Study Of Position Bias in Llm-as-a-judge, by Lin Shi et al.
Judging the Judges: A Systematic Study of Position Bias in LLM-as-a-Judge
by Lin Shi, Chiyu Ma, Wenhua Liang, Weicheng Ma, Soroush Vosoughi
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework introduces a systematic approach to examine position bias in pairwise comparisons of Large Language Models (LLMs) serving as judges. This study explores repetition stability, position consistency, and preference fairness across 12 LLM judges evaluating various tasks on MTBench and DevBench. The findings confirm that position bias is not due to random chance and highlights notable variations among judges and tasks. Furthermore, the quality gap between solutions significantly impacts position bias. These insights can inform debiasing strategies, optimize judge model selections, and improve benchmark design. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs are being used as judges to evaluate different tasks, but they have their own biases. One of these biases is called position bias, which means that the LLM tends to favor solutions based on where they appear in the prompt. This study shows how to examine this bias using a framework that looks at repetition stability, consistency, and fairness. The researchers tested 12 LLM judges across different tasks and found that position bias is not just random chance. They also discovered that the quality gap between solutions matters a lot. These findings can help make LLMs better judges. |
Keywords
» Artificial intelligence » Prompt
