Summary of Criticeval: Evaluating Large Language Model As Critic, by Tian Lan et al.
CriticEval: Evaluating Large Language Model as Critic
by Tian Lan, Wenwei Zhang, Chen Xu, Heyan Huang, Dahua Lin, Kai Chen, Xian-ling Mao
First submitted to arxiv on: 21 Feb 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 Large Language Models (LLMs) are crucial for self-improvement and scalable oversight. However, their ability to identify and rectify flaws in responses, known as critique ability, is still limited due to the lack of comprehensive evaluation methods. To address this issue, we introduce CriticEval, a novel benchmark designed to evaluate LLMs’ critique ability from four dimensions across nine diverse task scenarios. This includes evaluating both scalar-valued and textual critiques for responses of varying quality. A large number of annotated critiques serve as references, enabling reliable evaluation with GPT-4. Our experiments validate the reliability of CriticEval and demonstrate the promising potential of open-source LLMs, highlighting intriguing relationships between critique ability and factors like task types, response qualities, and critique dimensions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are important tools for helping themselves get better and being watched by computers. One key problem with these models is that they don’t always know when their answers are wrong and can’t fix them. To solve this issue, we created a new way to test how well language models can do this called CriticEval. It looks at four different ways the model might be wrong and checks nine different tasks where it could happen. We also tested some famous computer programs with GPT-4 to make sure our tests are reliable. Our results show that some open-source models work really well, but we still need to learn more about what makes them good or bad. |
Keywords
» Artificial intelligence » Gpt
