Summary of Benchmarking Llms’ Judgments with No Gold Standard, by Shengwei Xu et al.
Benchmarking LLMs’ Judgments with No Gold Standard
by Shengwei Xu, Yuxuan Lu, Grant Schoenebeck, Yuqing Kong
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 paper introduces GEM, a novel evaluation metric for assessing language generation by Large Language Models (LLMs), specifically in generating informative judgments. Unlike traditional machine translation and summarization tasks that rely on gold standard references, GEM allows for benchmarking LLM performance in subjective tasks without clear gold standards, such as academic peer review. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GEM is a new way to measure how well large language models can create useful information. Normally, we have gold standards to compare with, but sometimes we don’t. This paper shows that GEM can help us evaluate these models even when there’s no clear “right” answer. |
Keywords
* Artificial intelligence * Summarization * Translation
