Summary of Coarse-to-fine Highlighting: Reducing Knowledge Hallucination in Large Language Models, by Qitan Lv et al.
Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models
by Qitan Lv, Jie Wang, Hanzhu Chen, Bin Li, Yongdong Zhang, Feng Wu
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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 This paper proposes a novel method called COFT (COarse-to-Fine highlighting) that aims to reduce hallucination in language models by focusing on key texts at different granularity levels. The authors introduce a three-component system: recaller, scorer, and selector. Recaller extracts potential key entities using a knowledge graph, scorer measures their importance based on contextual weight, and selector selects high-weight entities with a dynamic threshold algorithm. COFT is tested on the knowledge hallucination benchmark, achieving a superior F1 score performance of over 30%. Additionally, it demonstrates versatility in various long-form tasks like reading comprehension and question answering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to help language models stop making up false information. It creates a special system called COFT that looks for important parts of text at different levels. The system has three steps: finding key words, judging their importance, and selecting the most important ones. This helps the model stay focused on what’s really important and avoid getting lost in too much detail. The results show that this method is very effective and can be used in a variety of tasks. |
Keywords
» Artificial intelligence » F1 score » Hallucination » Knowledge graph » Question answering
