Summary of Replacing Judges with Juries: Evaluating Llm Generations with a Panel Of Diverse Models, by Pat Verga et al.
Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models
by Pat Verga, Sebastian Hofstatter, Sophia Althammer, Yixuan Su, Aleksandra Piktus, Arkady Arkhangorodsky, Minjie Xu, Naomi White, Patrick Lewis
First submitted to arxiv on: 29 Apr 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 This research paper addresses the challenge of accurately evaluating the quality of Large Language Models (LLMs). Traditional methods rely on a single large model like GPT4, but this approach has limitations. The authors propose an alternative evaluation method called the Panel of LLM evaluators (PoLL), which uses a group of smaller models to judge the output of other models. In experiments across six datasets and three judge settings, the PoLL outperforms a single large judge in terms of accuracy, shows less bias, and is significantly more cost-effective. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper helps us figure out how well language models are doing their job. Right now, we don’t have a good way to test these models because they’re getting too smart for us. Instead, some people use one really powerful model to judge the work of other models. But this approach has its problems, like being too expensive and biased towards certain types of models. The authors suggest using a team of smaller models to do the judging instead. They tested this idea on six different datasets and found that it works better and is more affordable. |
