Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study over Open-ended Question Answering

• URL: http://arxiv.org/abs/2410.08085v1
• Date: Thu, 10 Oct 2024 16:29:21 GMT
• Title: Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study over Open-ended Question Answering
• Authors: Yuan Sui, Bryan Hooi,
• Abstract summary: We introduce OKGQA, a new benchmark specifically designed to assess Large Language Models (LLMs) enhanced with Knowledge Graphs (KGs) OKGQA is designed to closely reflect the complexities of practical applications using questions from different types, and incorporates specific metrics to measure both the reduction in hallucinations and the enhancement in reasoning capabilities. We also propose OKGQA-P to assess model performance when the semantics and structure of KGs are deliberately perturbed and contaminated.
• Score: 35.2451096137883
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent works integrating Knowledge Graphs (KGs) have led to promising improvements in enhancing reasoning accuracy of Large Language Models (LLMs). However, current benchmarks mainly focus on closed tasks, leaving a gap in the assessment of more complex, real-world scenarios. This gap has also obscured the evaluation of KGs' potential to mitigate the problem of hallucination in LLMs. To fill the gap, we introduce OKGQA, a new benchmark specifically designed to assess LLMs enhanced with KGs under open-ended, real-world question answering scenarios. OKGQA is designed to closely reflect the complexities of practical applications using questions from different types, and incorporates specific metrics to measure both the reduction in hallucinations and the enhancement in reasoning capabilities. To consider the scenario in which KGs may have varying levels of mistakes, we further propose another experiment setting OKGQA-P to assess model performance when the semantics and structure of KGs are deliberately perturbed and contaminated. OKGQA aims to (1) explore whether KGs can make LLMs more trustworthy in an open-ended setting, and (2) conduct a comparative analysis to shed light on methods and future directions for leveraging KGs to reduce LLMs' hallucination. We believe that this study can facilitate a more complete performance comparison and encourage continuous improvement in integrating KGs with LLMs.

Related papers

• OCEAN: Offline Chain-of-thought Evaluation and Alignment in Large Language Models [68.17018458283651]
  This work focuses on the offline evaluation of the chain-of-thought capabilities of LLMs. We use knowledge graphs (e.g., Wikidata5m) to provide feedback on the generated chain of thoughts. We show how to optimize LLMs based on the proposed evaluation method.
  arXiv  Detail & Related papers  (2024-10-31T07:48:44Z)
• Graph-constrained Reasoning: Faithful Reasoning on Knowledge Graphs with Large Language Models [83.28737898989694]
  Large language models (LLMs) struggle with faithful reasoning due to knowledge gaps and hallucinations. We introduce graph-constrained reasoning (GCR), a novel framework that bridges structured knowledge in KGs with unstructured reasoning in LLMs. GCR achieves state-of-the-art performance and exhibits strong zero-shot generalizability to unseen KGs without additional training.
  arXiv  Detail & Related papers  (2024-10-16T22:55:17Z)
• Tree-of-Traversals: A Zero-Shot Reasoning Algorithm for Augmenting Black-box Language Models with Knowledge Graphs [72.89652710634051]
  Knowledge graphs (KGs) complement Large Language Models (LLMs) by providing reliable, structured, domain-specific, and up-to-date external knowledge. We introduce Tree-of-Traversals, a novel zero-shot reasoning algorithm that enables augmentation of black-box LLMs with one or more KGs.
  arXiv  Detail & Related papers  (2024-07-31T06:01:24Z)
• EffiQA: Efficient Question-Answering with Strategic Multi-Model Collaboration on Knowledge Graphs [11.323661062578799]
  EffiQA consists of three stages: global planning, efficient KG exploration, and self-reflection. Empirical evidence on multiple KBQA benchmarks shows EffiQA's effectiveness. We hope the proposed new framework will pave the way for efficient, knowledge-intensive querying.
  arXiv  Detail & Related papers  (2024-06-03T11:56:07Z)
• Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering [87.67177556994525]
  We propose a training-free method called Generate-on-Graph (GoG) to generate new factual triples while exploring Knowledge Graphs (KGs) GoG performs reasoning through a Thinking-Searching-Generating framework, which treats LLM as both Agent and KG in IKGQA.
  arXiv  Detail & Related papers  (2024-04-23T04:47:22Z)
• Large Language Models Can Better Understand Knowledge Graphs Than We Thought [13.336418752729987]
  knowledge graph (KG) embeddings with model parameters become increasingly costly. Current prompting methods often rely on a trial-and-error approach. We show that unordered linearized triples are more effective for LLMs' understanding of KGs compared to fluent NL text.
  arXiv  Detail & Related papers  (2024-02-18T10:44:03Z)
• 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)
• Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning [104.92384929827776]
  Large language models (LLMs) have demonstrated impressive reasoning abilities in complex tasks. They lack up-to-date knowledge and experience hallucinations during reasoning. Knowledge graphs (KGs) offer a reliable source of knowledge for reasoning.
  arXiv  Detail & Related papers  (2023-10-02T10:14:43Z)
• Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answering [16.434098552925427]
  We study the KG-augmented language model approach for solving the knowledge graph question answering (KGQA) task. We propose an answer-sensitive KG-to-Text approach that can transform KG knowledge into well-textualized statements.
  arXiv  Detail & Related papers  (2023-09-20T10:42:08Z)

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.