Summary of Retrieval Meets Reasoning: Dynamic In-context Editing For Long-text Understanding, by Weizhi Fei et al.
Retrieval Meets Reasoning: Dynamic In-Context Editing for Long-Text Understanding
by Weizhi Fei, Xueyan Niu, Guoqing Xie, Yanhua Zhang, Bo Bai, Lei Deng, Wei Han
First submitted to arxiv on: 18 Jun 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 This paper presents a novel approach to Large Language Models (LLMs) that enables them to perform multi-hop reasoning within extensive textual contexts. The current limitations of LLMs are addressed by introducing dynamic in-context editing, inspired by breakthroughs in knowledge editing. This method treats lengthy contexts as malleable external knowledge and interactively gathers and integrates relevant information, allowing LLMs like Llama2 to engage in sophisticated reasoning steps with improved performance. The experimental results demonstrate that this approach outperforms state-of-the-art context window extrapolation methods and even compares favorably to more advanced commercial long-context models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help computers understand long texts by allowing them to get information from outside the text itself. Right now, these computers can only answer simple questions based on what they were taught beforehand. But this new method lets them find and add relevant details from other places in the text, making it easier for them to understand complex ideas. The computer’s performance improves a lot with this new approach, which is important because it could be used for things like summarizing long articles or understanding complex scientific papers. |
Keywords
» Artificial intelligence » Context window
