Leveraging Structured Information for Explainable Multi-hop Question Answering and Reasoning

• URL: http://arxiv.org/abs/2311.03734v1
• Date: Tue, 7 Nov 2023 05:32:39 GMT
• Title: Leveraging Structured Information for Explainable Multi-hop Question Answering and Reasoning
• Authors: Ruosen Li, Xinya Du
• Abstract summary: In this work, we investigate constructing and leveraging extracted semantic structures (graphs) for multi-hop question answering. Empirical results and human evaluations show that our framework: generates more faithful reasoning chains and substantially improves the QA performance on two benchmark datasets.
• Score: 14.219239732584368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural models, including large language models (LLMs), achieve superior performance on multi-hop question-answering. To elicit reasoning capabilities from LLMs, recent works propose using the chain-of-thought (CoT) mechanism to generate both the reasoning chain and the answer, which enhances the model's capabilities in conducting multi-hop reasoning. However, several challenges still remain: such as struggling with inaccurate reasoning, hallucinations, and lack of interpretability. On the other hand, information extraction (IE) identifies entities, relations, and events grounded to the text. The extracted structured information can be easily interpreted by humans and machines (Grishman, 2019). In this work, we investigate constructing and leveraging extracted semantic structures (graphs) for multi-hop question answering, especially the reasoning process. Empirical results and human evaluations show that our framework: generates more faithful reasoning chains and substantially improves the QA performance on two benchmark datasets. Moreover, the extracted structures themselves naturally provide grounded explanations that are preferred by humans, as compared to the generated reasoning chains and saliency-based explanations.

Related papers

• Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning [73.2950349728376]
  Large language models (LLMs) have demonstrated remarkable success across a wide range of tasks. However, they still encounter challenges in reasoning tasks that require understanding and inferring relationships between pieces of information. This challenge is particularly pronounced in tasks involving multi-step processes, such as logical reasoning and multi-hop question answering. We propose Reasoning with Graphs (RwG) by first constructing explicit graphs from the context.
  arXiv  Detail & Related papers  (2025-01-14T05:18:20Z)
• GRS-QA -- Graph Reasoning-Structured Question Answering Dataset [50.223851616680754]
  We introduce the Graph Reasoning-Structured Question Answering dataset (GRS-QA), which includes both semantic contexts and reasoning structures for QA pairs. Unlike existing M-QA datasets, GRS-QA explicitly captures intricate reasoning pathways by constructing reasoning graphs. Our empirical analysis reveals that LLMs perform differently when handling questions with varying reasoning structures.
  arXiv  Detail & Related papers  (2024-11-01T05:14:03Z)
• Make LLMs better zero-shot reasoners: Structure-orientated autonomous reasoning [52.83539473110143]
  We introduce a novel structure-oriented analysis method to help Large Language Models (LLMs) better understand a question. To further improve the reliability in complex question-answering tasks, we propose a multi-agent reasoning system, Structure-oriented Autonomous Reasoning Agents (SARA) Extensive experiments verify the effectiveness of the proposed reasoning system. Surprisingly, in some cases, the system even surpasses few-shot methods.
  arXiv  Detail & Related papers  (2024-10-18T05:30:33Z)
• 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)
• P-FOLIO: Evaluating and Improving Logical Reasoning with Abundant Human-Written Reasoning Chains [97.25943550933829]
  We present P-FOLIO, a human-annotated dataset consisting of diverse and complex reasoning chains. We use P-FOLIO to evaluate and improve large-language-model (LLM) reasoning capabilities.
  arXiv  Detail & Related papers  (2024-10-11T19:22:57Z)
• Even-if Explanations: Formal Foundations, Priorities and Complexity [18.126159829450028]
  We show that both linear and tree-based models are strictly more interpretable than neural networks. We introduce a preference-based framework that enables users to personalize explanations based on their preferences.
  arXiv  Detail & Related papers  (2024-01-17T11:38:58Z)
• STREET: A Multi-Task Structured Reasoning and Explanation Benchmark [56.555662318619135]
  We introduce a unified multi-task and multi-domain natural language reasoning and explanation benchmark. We expect models to not only answer questions, but also produce step-by-step structured explanations describing how premises in the question are used to produce intermediate conclusions that can prove the correctness of a certain answer.
  arXiv  Detail & Related papers  (2023-02-13T22:34:02Z)
• MetaLogic: Logical Reasoning Explanations with Fine-Grained Structure [129.8481568648651]
  We propose a benchmark to investigate models' logical reasoning capabilities in complex real-life scenarios. Based on the multi-hop chain of reasoning, the explanation form includes three main components. We evaluate the current best models' performance on this new explanation form.
  arXiv  Detail & Related papers  (2022-10-22T16:01:13Z)
• 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.