Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering

• URL: http://arxiv.org/abs/2403.02966v3
• Date: Wed, 09 Oct 2024 12:46:40 GMT
• Title: Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering
• Authors: Sungho Ko, Hyunjin Cho, Hyungjoo Chae, Jinyoung Yeo, Dongha Lee,
• Abstract summary: We propose EFSum, an Evidence-focused Fact Summarization framework for enhanced Quesetion Answering (QA) performance. Our experiments show that EFSum improves LLM's zero-shot QA performance.
• Score: 14.389264346634507
• License:
• Abstract: Recent studies have investigated utilizing Knowledge Graphs (KGs) to enhance Quesetion Answering (QA) performance of Large Language Models (LLMs), yet structured KG verbalization remains challengin. Existing methods, such as triple-form or free-form textual conversion of triple-form facts, encounter several issues. These include reduced evidence density due to duplicated entities or relationships, and reduced evidence clarity due to an inability to emphasize crucial evidence. To address these issues, we propose EFSum, an Evidence-focused Fact Summarization framework for enhanced QA with knowledge-augmented LLMs. We optimize an open-source LLM as a fact summarizer through distillation and preference alignment. Our extensive experiments show that EFSum improves LLM's zero-shot QA performance, and it is possible to ensure both the helpfulness and faithfulness of the summary.

Related papers

• GIVE: Structured Reasoning with Knowledge Graph Inspired Veracity Extrapolation [108.2008975785364]
  Graph Inspired Veracity Extrapolation (GIVE) is a novel reasoning framework that integrates the parametric and non-parametric memories. Our method facilitates a more logical and step-wise reasoning approach akin to experts' problem-solving, rather than gold answer retrieval.
  arXiv  Detail & Related papers  (2024-10-11T03:05:06Z)
• Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments [23.639378586798884]
  We propose retrieval augmented fact verification through the synthesis of contrasting arguments. Our method effectively retrieves relevant documents as evidence and evaluates arguments from varying perspectives. We demonstrate the effectiveness of our method through extensive experiments, where RAFTS can outperform GPT-based methods with a significantly smaller 7B LLM.
  arXiv  Detail & Related papers  (2024-06-14T08:13:34Z)
• BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidence [23.55601157586831]
  This paper introduces BIDER, an approach that refines retrieval documents into Key Supporting Evidence. We train BIDER by learning from crafting KSE, while maximizing its output to align with LLM's information acquisition preferences. Evaluations show BIDER boosts LLMs' answer quality by 7% while reducing input content length in retrieval documents by 80%, outperforming existing methods.
  arXiv  Detail & Related papers  (2024-02-19T14:28:31Z)
• C-ICL: Contrastive In-context Learning for Information Extraction [54.39470114243744]
  c-ICL is a novel few-shot technique that leverages both correct and incorrect sample constructions to create in-context learning demonstrations. Our experiments on various datasets indicate that c-ICL outperforms previous few-shot in-context learning methods.
  arXiv  Detail & Related papers  (2024-02-17T11:28:08Z)
• Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate [75.10515686215177]
  Large Language Models (LLMs) excel in text generation, but their capability for producing faithful explanations in fact-checking remains underexamined. We propose the Multi-Agent Debate Refinement (MADR) framework, leveraging multiple LLMs as agents with diverse roles. MADR ensures that the final explanation undergoes rigorous validation, significantly reducing the likelihood of unfaithful elements and aligning closely with the provided evidence.
  arXiv  Detail & Related papers  (2024-02-12T04:32:33Z)
• Knowledge Verification to Nip Hallucination in the Bud [69.79051730580014]
  We demonstrate the feasibility of mitigating hallucinations by verifying and minimizing the inconsistency between external knowledge present in the alignment data and the intrinsic knowledge embedded within foundation LLMs. We propose a novel approach called Knowledge Consistent Alignment (KCA), which employs a well-aligned LLM to automatically formulate assessments based on external knowledge. We demonstrate the superior efficacy of KCA in reducing hallucinations across six benchmarks, utilizing foundation LLMs of varying backbones and scales.
  arXiv  Detail & Related papers  (2024-01-19T15:39:49Z)
• HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs Responses [20.635793525894872]
  We develop a Hypothesis Knowledge Graph Enhanced (HyKGE) framework to improve the accuracy and reliability of Large Language Models (LLMs) Specifically, HyKGE explores the zero-shot capability and the rich knowledge of LLMs with Hypothesis Outputs to extend feasible exploration directions in the KGs. Experiments on two Chinese medical multiple-choice question datasets and one Chinese open-domain medical Q&A dataset with two LLM turbos demonstrate the superiority of HyKGE in terms of accuracy and explainability.
  arXiv  Detail & Related papers  (2023-12-26T04:49:56Z)
• Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting [51.7049140329611]
  This paper proposes Knowledge Graph-based Retrofitting (KGR) to mitigate factual hallucination during the reasoning process. Experiments show that KGR can significantly improve the performance of LLMs on factual QA benchmarks.
  arXiv  Detail & Related papers  (2023-11-22T11:08:38Z)
• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty [52.72790059506241]
  Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
  arXiv  Detail & Related papers  (2023-09-07T01:35:24Z)
• Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering [17.672572064705445]
  Large language models (LLMs) equipped with Chain-of-Thought (CoT) have shown impressive reasoning ability in various downstream tasks. We propose a framework called Knowledge-Driven Chain-of-Thought (KD-CoT) to verify and modify reasoning traces in CoT via interaction with external knowledge.
  arXiv  Detail & Related papers  (2023-08-25T09:23:55Z)

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.