Summary of Alleviating Hallucinations in Large Language Models with Scepticism Modeling, by Yetao Wu et al.
Alleviating Hallucinations in Large Language Models with Scepticism Modeling
by Yetao Wu, Yihong Wang, Teng Chen, Chenxi Liu, Ningyuan Xi, Qingqing Gu, Hongyang Lei, Zhonglin Jiang, Yong Chen, Luo Ji
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 In this paper, researchers tackle the issue of hallucinations in large language models (LLMs), which hinders their adoption in various fields. They propose a new approach called Skepticism Modeling (SM) to alleviate this problem. By combining token and logit information, SM formalizes self-estimation, allowing LLMs to better understand their own uncertainty. The authors construct doubt-emotion-aware data, pre-train the models continuously, and fine-tune them for improved self-estimation. Experimental results show that SM effectively enhances a model’s ability to estimate its uncertainty and generalizes well across tasks in out-of-domain experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are struggling with hallucinations, which prevents their use in many areas. A new way to make them better is needed. Researchers propose an approach called Skepticism Modeling (SM). SM helps LLMs understand when they’re unsure about something. They do this by combining information from words and logics. The team creates special data that shows how people feel emotions like doubt. Then, they train the models a bit differently to make them better at understanding themselves. The results show that SM makes LLMs more accurate and helps them work well on new tasks. |
Keywords
» Artificial intelligence » Token
