Towards Explainable Temporal Reasoning in Large Language Models: A Structure-Aware Generative Framework

• URL: http://arxiv.org/abs/2505.15245v1
• Date: Wed, 21 May 2025 08:20:35 GMT
• Title: Towards Explainable Temporal Reasoning in Large Language Models: A Structure-Aware Generative Framework
• Authors: Zihao Jiang, Ben Liu, Miao Peng, Wenjie Xu, Yao Xiao, Zhenyan Shan, Min Peng,
• Abstract summary: We introduce a benchmark covering a wide range of temporal granularities to systematically evaluate large language models' capabilities in explainable temporal reasoning.<n>We propose GETER, a novel structure-aware generative framework that integrates Graph structures with text for Explainable TEmporal Reasoning.<n> GETER achieves state-of-the-art performance while also demonstrating its effectiveness as well as strong generalization capabilities.
• Score: 18.770512964705176
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While large language models (LLMs) show great potential in temporal reasoning, most existing work focuses heavily on enhancing performance, often neglecting the explainable reasoning processes underlying the results. To address this gap, we introduce a comprehensive benchmark covering a wide range of temporal granularities, designed to systematically evaluate LLMs' capabilities in explainable temporal reasoning. Furthermore, our findings reveal that LLMs struggle to deliver convincing explanations when relying solely on textual information. To address challenge, we propose GETER, a novel structure-aware generative framework that integrates Graph structures with text for Explainable TEmporal Reasoning. Specifically, we first leverage temporal knowledge graphs to develop a temporal encoder that captures structural information for the query. Subsequently, we introduce a structure-text prefix adapter to map graph structure features into the text embedding space. Finally, LLMs generate explanation text by seamlessly integrating the soft graph token with instruction-tuning prompt tokens. Experimental results indicate that GETER achieves state-of-the-art performance while also demonstrating its effectiveness as well as strong generalization capabilities. Our dataset and code are available at https://github.com/carryTatum/GETER.

Related papers

• MetaMorph: Multimodal Understanding and Generation via Instruction Tuning [57.35160715164359]
  Visual-Predictive Instruction Tuning (VPiT) is a simple and effective extension to visual instruction tuning.<n>VPiT teaches an LLM to predict discrete text tokens and continuous visual tokens from any input sequence of image and text data.<n>We train our MetaMorph model and achieve competitive performance on both visual understanding and generation.
  arXiv  Detail & Related papers  (2024-12-18T18:58:50Z)
• Filter-then-Generate: Large Language Models with Structure-Text Adapter for Knowledge Graph Completion [20.973071287301067]
  Large Language Models (LLMs) present massive inherent knowledge and superior semantic comprehension capability.<n> Empirical evidence suggests that LLMs consistently perform worse than conventional knowledge graph completion approaches.<n>We propose a novel instruction-tuning-based method, namely FtG, to address these challenges.
  arXiv  Detail & Related papers  (2024-12-12T09:22:04Z)
• Struct-X: Enhancing Large Language Models Reasoning with Structured Data [38.558614152006975]
  Struct-X operates through five key phases: read-model-fill-reflect-reason'' It encodes structured data into a topological space using graph embeddings. It fills in missing entity information with knowledge retrieval modules. The final phase involves constructing a topological network with selected tokens.
  arXiv  Detail & Related papers  (2024-07-17T13:06:25Z)
• Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models [24.417129499480975]
  This paper first proposes a novel generative temporal knowledge graph question answering framework, GenTKGQA. First, we exploit LLM's intrinsic knowledge to mine temporal constraints and structural links in the questions without extra training. Next, we design virtual knowledge indicators to fuse the graph neural network signals of the subgraph and the text representations of the LLM in a non-shallow way.
  arXiv  Detail & Related papers  (2024-02-26T13:47:09Z)
• Structure Guided Prompt: Instructing Large Language Model in Multi-Step Reasoning by Exploring Graph Structure of the Text [44.81698187939784]
  This paper introduces Structure Guided Prompt, a framework designed to improve the multi-step reasoning capabilities of Large Language Models (LLMs) Our experiments show that this framework significantly enhances the reasoning capabilities of LLMs, enabling them to excel in a broader spectrum of natural language scenarios.
  arXiv  Detail & Related papers  (2024-02-20T22:56:23Z)
• DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text [73.68051228972024]
  Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
  arXiv  Detail & Related papers  (2023-10-31T04:37:57Z)
• Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why? [18.328637750057037]
  Large language models (LLMs) are gaining increasing attention for their capability to process graphs with rich text attributes. We aim to understand why the incorporation of structural information inherent in graph data can improve the prediction performance of LLMs.
  arXiv  Detail & Related papers  (2023-09-28T16:58:37Z)
• Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning [51.90524745663737]
  A key innovation is our use of explanations as features, which can be used to boost GNN performance on downstream tasks. Our method achieves state-of-the-art results on well-established TAG datasets. Our method significantly speeds up training, achieving a 2.88 times improvement over the closest baseline on ogbn-arxiv.
  arXiv  Detail & Related papers  (2023-05-31T03:18:03Z)
• StructGPT: A General Framework for Large Language Model to Reason over Structured Data [117.13986738340027]
  We develop an emphIterative Reading-then-Reasoning(IRR) approach for solving question answering tasks based on structured data. Our approach can significantly boost the performance of ChatGPT and achieve comparable performance against the full-data supervised-tuning baselines.
  arXiv  Detail & Related papers  (2023-05-16T17:45:23Z)
• Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction [57.854498238624366]
  We propose a retrieval-augmented approach, which retrieves schema-aware Reference As Prompt (RAP) for data-efficient knowledge graph construction. RAP can dynamically leverage schema and knowledge inherited from human-annotated and weak-supervised data as a prompt for each sample.
  arXiv  Detail & Related papers  (2022-10-19T16:40:28Z)
• Explaining Patterns in Data with Language Models via Interpretable Autoprompting [143.4162028260874]
  We introduce interpretable autoprompting (iPrompt), an algorithm that generates a natural-language string explaining the data. iPrompt can yield meaningful insights by accurately finding groundtruth dataset descriptions. Experiments with an fMRI dataset show the potential for iPrompt to aid in scientific discovery.
  arXiv  Detail & Related papers  (2022-10-04T18:32:14Z)

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.