Retrieval-enhanced Knowledge Editing in Language Models for Multi-Hop Question Answering

• URL: http://arxiv.org/abs/2403.19631v2
• Date: Tue, 13 Aug 2024 19:34:13 GMT
• Title: Retrieval-enhanced Knowledge Editing in Language Models for Multi-Hop Question Answering
• Authors: Yucheng Shi, Qiaoyu Tan, Xuansheng Wu, Shaochen Zhong, Kaixiong Zhou, Ninghao Liu,
• Abstract summary: Large Language Models (LLMs) have shown proficiency in question-answering tasks but often struggle to integrate real-time knowledge. We propose the Retrieval-Augmented model Editing (RAE) framework for multi-hop question answering.
• Score: 47.199078631274745
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large Language Models (LLMs) have shown proficiency in question-answering tasks but often struggle to integrate real-time knowledge, leading to potentially outdated or inaccurate responses. This problem becomes even more challenging when dealing with multi-hop questions, since they require LLMs to update and integrate multiple knowledge pieces relevant to the questions. To tackle the problem, we propose the Retrieval-Augmented model Editing (RAE) framework for multi-hop question answering. RAE first retrieves edited facts and then refines the language model through in-context learning. Specifically, our retrieval approach, based on mutual information maximization, leverages the reasoning abilities of LLMs to identify chain facts that traditional similarity-based searches might miss. In addition, our framework includes a pruning strategy to eliminate redundant information from the retrieved facts, which enhances the editing accuracy and mitigates the hallucination problem. Our framework is supported by theoretical justification for its fact retrieval efficacy. Finally, comprehensive evaluation across various LLMs validates RAE's ability in providing accurate answers with updated knowledge. Our code is available at: https://github.com/sycny/RAE.

Related papers

• MQA-KEAL: Multi-hop Question Answering under Knowledge Editing for Arabic Language [7.488965571323756]
  We propose Multi-hop Questioning Answering under Knowledge Editing for Arabic Language (MQA-KEAL) MQA-KEAL stores knowledge edits as structured knowledge units in the external memory. We also contribute MQUAKE-AR (Arabic translation of English benchmark MQUAKE) as well as a new benchmark MQA-AEVAL for rigorous performance evaluation of MQA under KE for Arabic language.
  arXiv  Detail & Related papers  (2024-09-18T18:40:02Z)
• LLM-Based Multi-Hop Question Answering with Knowledge Graph Integration in Evolving Environments [35.3938477255058]
  This paper introduces Graph Memory-based Editing for Large Language Models (GMeLLo) GMeLLo merges the explicit knowledge representation of Knowledge Graphs with the linguistic flexibility of Large Language Models. Our results show that GMeLLo significantly surpasses current state-of-the-art knowledge editing methods in the multi-hop question answering benchmark, MQuAKE.
  arXiv  Detail & Related papers  (2024-08-28T16:15:45Z)
• Enhancing Multi-hop Reasoning through Knowledge Erasure in Large Language Model Editing [38.590823330865845]
  Large language models (LLMs) face challenges with internal knowledge inaccuracies and outdated information. Knowledge editing has emerged as a pivotal approach to mitigate these issues. We propose a novel knowledge editing method that incorporates a Knowledge Erasure mechanism for Large language model Editing (KELE)
  arXiv  Detail & Related papers  (2024-08-22T14:53:33Z)
• Robust and Scalable Model Editing for Large Language Models [75.95623066605259]
  We propose EREN (Edit models by REading Notes) to improve the scalability and robustness of LLM editing. Unlike existing techniques, it can integrate knowledge from multiple edits, and correctly respond to syntactically similar but semantically unrelated inputs.
  arXiv  Detail & Related papers  (2024-03-26T06:57:23Z)
• A Comprehensive Study of Knowledge Editing for Large Language Models [82.65729336401027]
  Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. This paper defines the knowledge editing problem and provides a comprehensive review of cutting-edge approaches. We introduce a new benchmark, KnowEdit, for a comprehensive empirical evaluation of representative knowledge editing approaches.
  arXiv  Detail & Related papers  (2024-01-02T16:54:58Z)
• PokeMQA: Programmable knowledge editing for Multi-hop Question Answering [46.80110170981976]
  Multi-hop question answering (MQA) is one of the challenging tasks to evaluate machine's comprehension and reasoning abilities. We propose a framework, Programmable knowledge editing for Multi-hop Question Answering (MQA) Specifically, we prompt LLMs to decompose knowledge-augmented multi-hop question, while interacting with a detached trainable scope detector to modulate LLMs behavior depending on external conflict signal.
  arXiv  Detail & Related papers  (2023-12-23T08:32:13Z)
• FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation [92.43001160060376]
  We study the factuality of large language models (LLMs) in the context of answering questions that test current world knowledge. We introduce FreshQA, a novel dynamic QA benchmark encompassing a diverse range of question and answer types. We benchmark a diverse array of both closed and open-source LLMs under a two-mode evaluation procedure that allows us to measure both correctness and hallucination. Motivated by these results, we present FreshPrompt, a simple few-shot prompting method that substantially boosts the performance of an LLM on FreshQA.
  arXiv  Detail & Related papers  (2023-10-05T00:04:12Z)
• Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks [121.74957524305283]
  This paper proposes a novel framework named textbfSearch-in-the-Chain (SearChain) for the interaction between Information Retrieval (IR) and Large Language Model (LLM) Experiments show that SearChain outperforms state-of-the-art baselines on complex knowledge-intensive tasks.
  arXiv  Detail & Related papers  (2023-04-28T10:15:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.