Summary of A Survey on the Honesty Of Large Language Models, by Siheng Li et al.
A Survey on the Honesty of Large Language Models
by Siheng Li, Cheng Yang, Taiqiang Wu, Chufan Shi, Yuji Zhang, Xinyu Zhu, Zesen Cheng, Deng Cai, Mo Yu, Lemao Liu, Jie Zhou, Yujiu Yang, Ngai Wong, Xixin Wu, Wai Lam
First submitted to arxiv on: 27 Sep 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 Medium Difficulty summary: Large Language Models (LLMs) are designed to recognize what they know and don’t know, and express their knowledge faithfully. However, current LLMs still exhibit dishonest behaviors, such as confidently presenting wrong answers or failing to express known information. Researchers face challenges in defining honesty, distinguishing between known and unknown knowledge, and understanding related research. This paper provides a comprehensive survey on the honesty of LLMs, including its clarification, evaluation approaches, and strategies for improvement. The paper also offers insights for future research, aiming to inspire further exploration in this important area. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Large Language Models are special computer programs that can understand and generate human-like text. But these models sometimes get things wrong or hide what they know. Scientists have trouble defining what “honesty” means in this context and figuring out how to measure it. This paper helps clarify the issue by summarizing current research on honest language models, including ways to evaluate them and make them better. The goal is to encourage more research on this important topic. |
