Summary of Judgebench: a Benchmark For Evaluating Llm-based Judges, by Sijun Tan et al.
JudgeBench: A Benchmark for Evaluating LLM-based Judges
by Sijun Tan, Siyuan Zhuang, Kyle Montgomery, William Y. Tang, Alejandro Cuadron, Chenguang Wang, Raluca Ada Popa, Ion Stoica
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 evaluates the reliability of LLM-based judges, which have become a scalable alternative to human evaluation for assessing and improving models. The existing benchmarks focus on aligning with human preferences but often fail to account for more challenging tasks that require factual and logical correctness. To address this, JudgeBench is introduced as a novel benchmark for evaluating LLM-based judges on response pairs spanning knowledge, reasoning, math, and coding. JudgeBench leverages a pipeline for converting existing datasets into challenging response pairs with preference labels reflecting objective correctness. The evaluation shows that JudgeBench poses a greater challenge than previous benchmarks, with many strong models performing just slightly better than random guessing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLLM-based judges are like super smart AI helpers that can help us assess and improve other AI models. But how good are these judges themselves? Researchers didn’t really check if these judges are reliable or not. As these AI judges get smarter, they start giving more complex answers that need to be evaluated carefully. Existing tests mostly look at whether the judge agrees with human preferences, but this might not work for harder tasks where humans can’t even agree on what’s correct. To fix this, scientists created a new test called JudgeBench that evaluates these AI judges on tricky questions that cover many topics like math, coding, and more. They found that even top-notch models don’t do much better than just guessing randomly. |
