Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts

• URL: http://arxiv.org/abs/2405.06524v1
• Date: Fri, 10 May 2024 15:10:20 GMT
• Title: Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts
• Authors: Wenyu Huang, Guancheng Zhou, Mirella Lapata, Pavlos Vougiouklis, Sebastien Montella, Jeff Z. Pan,
• Abstract summary: Large Language Models (LLMs) are effective in performing various NLP tasks, but struggle to handle tasks that require extensive, real-world knowledge. We propose a benchmark that requires knowledge of long-tail facts for answering the involved questions. Our experiments show that LLMs alone struggle with answering these questions, especially when the long-tail level is high or rich knowledge is required.
• Score: 50.06633829833144
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Although Large Language Models (LLMs) are effective in performing various NLP tasks, they still struggle to handle tasks that require extensive, real-world knowledge, especially when dealing with long-tail facts (facts related to long-tail entities). This limitation highlights the need to supplement LLMs with non-parametric knowledge. To address this issue, we analysed the effects of different types of non-parametric knowledge, including textual passage and knowledge graphs (KGs). Since LLMs have probably seen the majority of factual question-answering datasets already, to facilitate our analysis, we proposed a fully automatic pipeline for creating a benchmark that requires knowledge of long-tail facts for answering the involved questions. Using this pipeline, we introduce the LTGen benchmark. We evaluate state-of-the-art LLMs in different knowledge settings using the proposed benchmark. Our experiments show that LLMs alone struggle with answering these questions, especially when the long-tail level is high or rich knowledge is required. Nonetheless, the performance of the same models improved significantly when they were prompted with non-parametric knowledge. We observed that, in most cases, prompting LLMs with KG triples surpasses passage-based prompting using a state-of-the-art retriever. In addition, while prompting LLMs with both KG triples and documents does not consistently improve knowledge coverage, it can dramatically reduce hallucinations in the generated content.

Related papers

• Reasoning on Efficient Knowledge Paths:Knowledge Graph Guides Large Language Model for Domain Question Answering [18.94220625114711]
  Large language models (LLMs) perform surprisingly well and outperform human experts on many tasks. This paper integrates and optimized a pipeline for selecting reasoning paths from KG based on LLM. We also propose a simple and effective subgraph retrieval method based on chain of thought (CoT) and page rank.
  arXiv  Detail & Related papers  (2024-04-16T08:28:16Z)
• 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)
• Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs [60.40396361115776]
  This paper introduces a novel collaborative approach, namely SlimPLM, that detects missing knowledge in large language models (LLMs) with a slim proxy model. We employ a proxy model which has far fewer parameters, and take its answers as answers. Heuristic answers are then utilized to predict the knowledge required to answer the user question, as well as the known and unknown knowledge within the LLM.
  arXiv  Detail & Related papers  (2024-02-19T11:11:08Z)
• KnowledgeNavigator: Leveraging Large Language Models for Enhanced Reasoning over Knowledge Graph [11.808990571175269]
  Large language model (LLM) has achieved outstanding performance on various downstream tasks with its powerful natural language understanding and zero-shot capability, but LLM still suffers from knowledge limitation. We propose a novel framework KnowledgeNavigator to address these challenges by efficiently and accurately retrieving external knowledge from knowledge graph. We evaluate KnowledgeNavigator on multiple public KGQA benchmarks, the experiments show the framework has great effectiveness and generalization.
  arXiv  Detail & Related papers  (2023-12-26T04:22:56Z)
• Investigating Answerability of LLMs for Long-Form Question Answering [35.41413072729483]
  We focus on long-form question answering (LFQA) because it has several practical and impactful applications. We propose a question-generation method from abstractive summaries and show that generating follow-up questions from summaries of long documents can create a challenging setting.
  arXiv  Detail & Related papers  (2023-09-15T07:22:56Z)
• Eva-KELLM: A New Benchmark for Evaluating Knowledge Editing of LLMs [54.22416829200613]
  Eva-KELLM is a new benchmark for evaluating knowledge editing of large language models. Experimental results indicate that the current methods for knowledge editing using raw documents are not effective in yielding satisfactory results.
  arXiv  Detail & Related papers  (2023-08-19T09:17:19Z)
• Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation [91.30946119104111]
  We show that large language models (LLMs) possess unwavering confidence in their capabilities to respond to questions. Retrieval augmentation proves to be an effective approach in enhancing LLMs' awareness of knowledge boundaries. We also find that LLMs have a propensity to rely on the provided retrieval results when formulating answers.
  arXiv  Detail & Related papers  (2023-07-20T16:46:10Z)
• Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering [7.888547093390469]
  Large Language Models (LLMs) are capable of performing zero-shot closed-book question answering tasks. We propose to augment the knowledge directly in the input of LLMs. Our framework, Knowledge-Augmented language model PromptING (KAPING), requires no model training, thus completely zero-shot.
  arXiv  Detail & Related papers  (2023-06-07T04:15:21Z)
• Augmented Large Language Models with Parametric Knowledge Guiding [72.71468058502228]
  Large Language Models (LLMs) have significantly advanced natural language processing (NLP) with their impressive language understanding and generation capabilities. Their performance may be suboptimal for domain-specific tasks that require specialized knowledge due to limited exposure to the related data. We propose the novel Parametric Knowledge Guiding (PKG) framework, which equips LLMs with a knowledge-guiding module to access relevant knowledge.
  arXiv  Detail & Related papers  (2023-05-08T15:05:16Z)

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.