学习随时间推移进行推理：用于改进语言模型时间推理的时间线自我反思
Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models
AI总结：
本文提出TISER框架，通过结合时间线构建与迭代自我反思的多阶段过程，利用测试时扩展延长推理轨迹，有效增强大型语言模型的时间推理能力，使较小开源模型在时间推理任务上超越较大闭权重模型。
摘要：
大型语言模型（LLMs）已成为生成连贯文本、理解上下文和执行推理任务的强大工具。然而，它们在时间推理方面存在困难，这需要处理诸如事件排序、持续时间和时间间关系等与时间相关的信息。这些能力对于问答、调度和历史分析等应用至关重要。在本文中，我们引入了TISER，这是一个新颖的框架，通过结合时间线构建和迭代自我反思的多阶段过程来增强LLMs的时间推理能力。我们的方法利用测试时扩展来延长推理轨迹的长度，使模型能够更有效地捕获复杂的时间依赖关系。该策略不仅提高了推理准确性，还改善了推理过程的可追溯性。实验结果证明了在多个基准测试（包括分布外测试集）上的最先进性能，并表明TISER使较小的开源模型能够在具有挑战性的时间推理任务上超越较大的闭权重模型。
英文摘要：
Large Language Models (LLMs) have emerged as powerful tools for generating coherent text, understanding context, and performing reasoning tasks. However, they struggle with temporal reasoning, which requires processing time-related information such as event sequencing, durations, and inter-temporal relationships. These capabilities are critical for applications including question answering, scheduling, and historical analysis. In this paper, we introduce TISER, a novel framework that enhances the temporal reasoning abilities of LLMs through a multi-stage process that combines timeline construction with iterative self-reflection. Our approach leverages test-time scaling to extend the length of reasoning traces, enabling models to capture complex temporal dependencies more effectively. This strategy not only boosts reasoning accuracy but also improves the traceability of the inference process. Experimental results demonstrate state-of-the-art performance across multiple benchmarks, including out-of-distribution test sets, and reveal that TISER enables smaller open-source models to surpass larger closed-weight models on challenging temporal reasoning tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
