让大模型根据问题难度自动调整时间推理步骤，提升准确性。
AdapTime: Enabling Adaptive Temporal Reasoning in Large Language Models
- 根据输入动态选择重述、重写或复核三种推理动作。
- 在多个时间推理数据集上显著超越固定流程方法。
- 适合需要灵活处理时间逻辑的复杂问答场景。
大语言模型在通用知识问答中表现出强大的推理能力，但在处理时间信息方面仍显不足。现有方法通常依赖外部工具或人工验证，且针对特定场景设计，泛化能力差。此外，这些方法对所有问题采用固定流程，忽视了不同类型的时间问题需不同推理策略——简单问题被过度处理，复杂问题则推理不足。为此，我们提出AdapTime，一种自适应时间推理方法，可根据输入上下文动态执行推理步骤。该方法包含三种时间推理动作：重述、重写和复核，由一个LLM规划器引导推理过程。AdapTime可无缝集成至当前最先进的大语言模型，显著提升其时间推理能力，且无需外部支持。大量实验验证了该方法的有效性。
原文摘要 · Abstract (English)
Large language models have demonstrated strong reasoning capabilities in general knowledge question answering. However, their ability to handle temporal information remains limited. To address this limitation, existing approaches often involve external tools or manual verification and are tailored to specific scenarios, leading to poor generalizability. Moreover, these methods apply a fixed pipeline to all questions, overlooking the fact that different types of temporal questions require distinct reasoning strategies, which leads to unnecessary processing for simple cases and inadequate reasoning for complex ones. To this end, we propose AdapTime, an adaptive temporal reasoning method that dynamically executes reasoning steps based on the input context. Specifically, it involves three temporal reasoning actions: reformulate, rewrite and review, with an LLM planner guiding the reasoning process. AdapTime integrates seamlessly with state-of-the-art LLMs and significantly enhances their temporal reasoning capabilities without relying on external support. Extensive experiments demonstrate the effectiveness of our approach.
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