Internal and External Knowledge Interactive Refinement Framework for Knowledge-Intensive Question Answering

• URL: http://arxiv.org/abs/2408.12979v1
• Date: Fri, 23 Aug 2024 10:52:57 GMT
• Title: Internal and External Knowledge Interactive Refinement Framework for Knowledge-Intensive Question Answering
• Authors: Haowei Du, Dongyan Zhao,
• Abstract summary: We propose a new internal and external knowledge interactive refinement paradigm dubbed IEKR. By simply adding a prompt like 'Tell me something about' to the LLMs, we try to review related explicit knowledge and insert them with the query into the retriever for external retrieval.
• Score: 33.89176174108559
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent works have attempted to integrate external knowledge into LLMs to address the limitations and potential factual errors in LLM-generated content. However, how to retrieve the correct knowledge from the large amount of external knowledge imposes a challenge. To this end, we empirically observe that LLMs have already encoded rich knowledge in their pretrained parameters and utilizing these internal knowledge improves the retrieval of external knowledge when applying them to knowledge-intensive tasks. In this paper, we propose a new internal and external knowledge interactive refinement paradigm dubbed IEKR to utilize internal knowledge in LLM to help retrieve relevant knowledge from the external knowledge base, as well as exploit the external knowledge to refine the hallucination of generated internal knowledge. By simply adding a prompt like 'Tell me something about' to the LLMs, we try to review related explicit knowledge and insert them with the query into the retriever for external retrieval. The external knowledge is utilized to complement the internal knowledge into input of LLM for answers. We conduct experiments on 3 benchmark datasets in knowledge-intensive question answering task with different LLMs and domains, achieving the new state-of-the-art. Further analysis shows the effectiveness of different modules in our approach.

Related papers

• Evaluating the External and Parametric Knowledge Fusion of Large Language Models [72.40026897037814]
  We develop a systematic pipeline for data construction and knowledge infusion to simulate knowledge fusion scenarios. Our investigation reveals that enhancing parametric knowledge within LLMs can significantly bolster their capability for knowledge integration. Our findings aim to steer future explorations on harmonizing external and parametric knowledge within LLMs.
  arXiv  Detail & Related papers  (2024-05-29T11:48:27Z)
• Untangle the KNOT: Interweaving Conflicting Knowledge and Reasoning Skills in Large Language Models [51.72963030032491]
  Knowledge documents for large language models (LLMs) may conflict with the memory of LLMs due to outdated or incorrect knowledge. We construct a new dataset, dubbed KNOT, for knowledge conflict resolution examination in the form of question answering.
  arXiv  Detail & Related papers  (2024-04-04T16:40:11Z)
• InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration [61.554209059971576]
  Large Language Models (LLMs) have shown remarkable open-generation capabilities across diverse domains. Injecting new knowledge poses the risk of forgetting previously acquired knowledge. We propose a novel Infuser-Guided Knowledge Integration framework.
  arXiv  Detail & Related papers  (2024-02-18T03:36:26Z)
• RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge [69.79676144482792]
  This study aims to evaluate the ability of LLMs to distinguish reliable information from external knowledge. Our benchmark consists of two tasks, Question Answering and Text Generation, and for each task, we provide models with a context containing counterfactual information.
  arXiv  Detail & Related papers  (2023-11-14T13:24:19Z)
• "Merge Conflicts!" Exploring the Impacts of External Distractors to Parametric Knowledge Graphs [15.660128743249611]
  Large language models (LLMs) acquire extensive knowledge during pre-training, known as their parametric knowledge. LLMs inevitably require external knowledge during their interactions with users. This raises a crucial question: How will LLMs respond when external knowledge interferes with their parametric knowledge?
  arXiv  Detail & Related papers  (2023-09-15T17:47:59Z)
• Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation [109.8527403904657]
  We show that large language models (LLMs) possess unwavering confidence in their knowledge and cannot handle the conflict between internal and external knowledge well. Retrieval augmentation proves to be an effective approach in enhancing LLMs' awareness of knowledge boundaries. We propose a simple method to dynamically utilize supporting documents with our judgement strategy.
  arXiv  Detail & Related papers  (2023-07-20T16:46:10Z)
• Knowledge Rumination for Pre-trained Language Models [77.55888291165462]
  We propose a new paradigm dubbed Knowledge Rumination to help the pre-trained language model utilize related latent knowledge without retrieving it from the external corpus. We apply the proposed knowledge rumination to various language models, including RoBERTa, DeBERTa, and GPT-3.
  arXiv  Detail & Related papers  (2023-05-15T15:47:09Z)

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.