Summary of Retrieval-enhanced Knowledge Editing in Language Models For Multi-hop Question Answering, by Yucheng Shi et al.
Retrieval-enhanced Knowledge Editing in Language Models for Multi-Hop Question Answering
by Yucheng Shi, Qiaoyu Tan, Xuansheng Wu, Shaochen Zhong, Kaixiong Zhou, Ninghao Liu
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The proposed Retrieval-Augmented model Editing (RAE) framework aims to improve Large Language Models’ (LLMs’) ability to integrate real-time knowledge and provide accurate responses to multi-hop questions. The RAE framework retrieves edited facts through mutual information maximization, leveraging the reasoning abilities of LLMs to identify chain facts that traditional similarity-based searches might miss. Additionally, a pruning strategy is used to eliminate redundant information from retrieved facts, enhancing editing accuracy and mitigating the hallucination problem. Comprehensive evaluation across various LLMs validates RAE’s ability in providing accurate answers with updated knowledge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are really good at answering questions, but sometimes they don’t have the most up-to-date information. This is a big problem when we want them to answer complex, multi-step questions that require lots of different pieces of information. To solve this issue, researchers created a new way to help LLMs get better at understanding and answering these kinds of questions. The approach uses mutual information maximization to find the most important information needed to answer a question. This helps eliminate redundant or outdated information and provides more accurate answers. |
Keywords
* Artificial intelligence * Hallucination * Pruning
