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Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering

Wanqi Yang, Yanda Li, Meng Fang, Ling Chen

TL;DR

This paper proposes a novel framework that enhances temporal awareness and reasoning through Temporal Information-Aware Embedding and Granular Contrastive Reinforcement Learning and demonstrates that this framework significantly outperforms existing LLMs in TSQA tasks.

Abstract

Time-Sensitive Question Answering (TSQA) demands the effective utilization of specific temporal contexts, encompassing multiple time-evolving facts, to address time-sensitive questions. This necessitates not only the parsing of temporal information within questions but also the identification and understanding of time-evolving facts to generate accurate answers. However, current large language models still have limited sensitivity to temporal information and their inadequate temporal reasoning capabilities. In this paper, we propose a novel framework that enhances temporal awareness and reasoning through Temporal Information-Aware Embedding and Granular Contrastive Reinforcement Learning. Experimental results on four TSQA datasets demonstrate that our framework significantly outperforms existing LLMs in TSQA tasks, marking a step forward in bridging the performance gap between machine and human temporal understanding and reasoning.

Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering

TL;DR

This paper proposes a novel framework that enhances temporal awareness and reasoning through Temporal Information-Aware Embedding and Granular Contrastive Reinforcement Learning and demonstrates that this framework significantly outperforms existing LLMs in TSQA tasks.

Abstract

Time-Sensitive Question Answering (TSQA) demands the effective utilization of specific temporal contexts, encompassing multiple time-evolving facts, to address time-sensitive questions. This necessitates not only the parsing of temporal information within questions but also the identification and understanding of time-evolving facts to generate accurate answers. However, current large language models still have limited sensitivity to temporal information and their inadequate temporal reasoning capabilities. In this paper, we propose a novel framework that enhances temporal awareness and reasoning through Temporal Information-Aware Embedding and Granular Contrastive Reinforcement Learning. Experimental results on four TSQA datasets demonstrate that our framework significantly outperforms existing LLMs in TSQA tasks, marking a step forward in bridging the performance gap between machine and human temporal understanding and reasoning.
Paper Structure (28 sections, 8 equations, 2 figures, 8 tables)

This paper contains 28 sections, 8 equations, 2 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Problem Definition
  3. Method
  4. Temporal Information-Aware Embedding
  5. Granular Contrastive Reinforcement Learning
  6. Negative Answers with Different Granularity.
  7. Contrastive Reinforcement Learning.
  8. Experiments
  9. Experimental Setup
  10. Dataset
  11. Training
  12. Evaluation Metrics
  13. Baselines
  14. Experimental Results
  15. Ablation Study
  16. ...and 13 more sections

Figures (2)

  • Figure 1: An overview of the TSQA task with our framework.
  • Figure 2: The architecture of our framework (Left: Temporal Information-Aware Embedding;Right: Granular Contrastive Reinforcement Learning).