FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering

• URL: http://arxiv.org/abs/2405.13873v2
• Date: Thu, 10 Oct 2024 15:27:41 GMT
• Title: FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering
• Authors: Yuan Sui, Yufei He, Nian Liu, Xiaoxin He, Kun Wang, Bryan Hooi,
• Abstract summary: We propose a retrieval augmented reasoning method, FiDeLiS, which enhances knowledge graph question answering. FiDeLiS uses a keyword-enhanced retrieval mechanism that fetches relevant entities and relations from a vector-based index of KGs. A distinctive feature of our approach is its blend of natural language planning with beam search to optimize the selection of reasoning paths.
• Score: 46.41364317172677
• License:
• Abstract: Large language models are often challenged by generating erroneous or `hallucinated' responses, especially in complex reasoning tasks. To mitigate this, we propose a retrieval augmented reasoning method, FiDeLiS, which enhances knowledge graph question answering by anchoring responses to structured, verifiable reasoning paths. FiDeLiS uses a keyword-enhanced retrieval mechanism that fetches relevant entities and relations from a vector-based index of KGs to ensure high-recall retrieval. Once these entities and relations are retrieved, our method constructs candidate reasoning paths which are then refined using a stepwise beam search. This ensures that all the paths we create can be confidently linked back to KGs, ensuring they are accurate and reliable. A distinctive feature of our approach is its blend of natural language planning with beam search to optimize the selection of reasoning paths. Moreover, we redesign the way reasoning paths are scored by transforming this process into a deductive reasoning task, allowing the LLM to assess the validity of the paths through deductive reasoning rather than traditional logit-based scoring. This helps avoid misleading reasoning chains and reduces unnecessary computational demand. Extensive experiments demonstrate that our method, even as a training-free method which has lower computational costs and superior generality, outperforms established strong baselines across three datasets.

Related papers

• Causal Reasoning in Large Language Models: A Knowledge Graph Approach [6.5344638992876085]
  Large language models (LLMs) typically improve performance by either retrieving semantically similar information, or enhancing reasoning abilities through structured prompts like chain-of-thought. This paper proposes a knowledge graph (KG)-based random-walk reasoning approach that leverages causal relationships.
  arXiv  Detail & Related papers  (2024-10-15T13:24:44Z)
• 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)
• Thought-Path Contrastive Learning via Premise-Oriented Data Augmentation for Logical Reading Comprehension [9.67774998354062]
  Previous research has primarily focused on enhancing logical reasoning capabilities through Chain-of-Thought (CoT) or data augmentation. We propose a Premise-Oriented Data Augmentation (PODA) framework to generate CoT rationales including analyses for both correct and incorrect options. We also introduce a novel thought-path contrastive learning method that compares reasoning paths between the original and counterfactual samples.
  arXiv  Detail & Related papers  (2024-09-22T15:44:43Z)
• PathFinder: Guided Search over Multi-Step Reasoning Paths [80.56102301441899]
  We propose PathFinder, a tree-search-based reasoning path generation approach. It enhances diverse branching and multi-hop reasoning through the integration of dynamic decoding. Our model generalizes well to longer, unseen reasoning chains, reflecting similar complexities to beam search with large branching factors.
  arXiv  Detail & Related papers  (2023-12-08T17:05:47Z)
• LaRS: Latent Reasoning Skills for Chain-of-Thought Reasoning [61.7853049843921]
  Chain-of-thought (CoT) prompting is a popular in-context learning approach for large language models (LLMs) This paper introduces a new approach named Latent Reasoning Skills (LaRS) that employs unsupervised learning to create a latent space representation of rationales.
  arXiv  Detail & Related papers  (2023-12-07T20:36:10Z)
• Graph Elicitation for Guiding Multi-Step Reasoning in Large Language Models [16.432208223793666]
  Chain-of-Thought prompting along with sub-question generation and answering has enhanced multi-step reasoning capabilities. We propose a GE-Reasoning method, which directs Large Language Models to generate proper sub-questions and corresponding answers. Our approach outperforms previous CoT prompting methods and their variants on multi-hop question answering benchmark datasets.
  arXiv  Detail & Related papers  (2023-11-16T10:36:08Z)
• 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)
• Boosting Language Models Reasoning with Chain-of-Knowledge Prompting [18.326858925174605]
  Chain-of-Knowledge (CoK) prompting aims at eliciting explicit pieces of knowledge evidence in the form of structure triple. Benefiting from CoK, we additionally introduce a F2-Verification method to estimate the reliability of the reasoning chains. Extensive experiments demonstrate that our method can further improve the performance of commonsense, factual, symbolic, and arithmetic reasoning tasks.
  arXiv  Detail & Related papers  (2023-06-10T12:42:36Z)
• Complexity-Based Prompting for Multi-Step Reasoning [72.0057198610614]
  We study the task of prompting large-scale language models to perform multi-step reasoning. A central question is which reasoning examples make the most effective prompts. We propose complexity-based prompting, a simple and effective example selection scheme for multi-step reasoning.
  arXiv  Detail & Related papers  (2022-10-03T05:33:27Z)
• Faithful Reasoning Using Large Language Models [12.132449274592668]
  We show how LMs can be made to perform faithful multi-step reasoning via a process whose causal structure mirrors the underlying logical structure of the problem. Our approach works by chaining together reasoning steps, where each step results from calls to two fine-tuned LMs. We demonstrate the effectiveness of our model on multi-step logical deduction and scientific question-answering, showing that it outperforms baselines on final answer accuracy.
  arXiv  Detail & Related papers  (2022-08-30T13:44:41Z)

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.