思维链至关重要：通过推理路径监督改进长上下文语言模型
Chain-of-Thought Matters: Improving Long-Context Language Models with Reasoning Path Supervision
AI总结：
本文提出LongRePS框架，通过过程监督和自采样生成高质量推理路径，显著提升长上下文语言模型在领域内及跨领域问答任务上的性能。
摘要：
近期大型语言模型（LLMs）的进展凸显了处理长上下文任务的挑战，在此类任务中，模型需要在广泛的输入上下文中进行推理以聚合目标信息。尽管思维链（CoT）提示在多步推理中展现出潜力，但其在长上下文场景中的有效性仍未得到充分探索。通过对多种任务的系统研究，我们证明CoT的优势可推广至大多数长上下文场景，并随上下文长度增加而增强。基于这一关键观察，我们提出LongRePS，一个过程监督框架，教导模型生成高质量的推理路径以提升长上下文性能。该框架包含一种自采样机制以引导推理路径，以及一种专为长上下文场景设计的新型质量评估协议。在多个长上下文基准上的实验结果表明了我们方法的有效性，在领域内任务（LLaMA/Qwen在MuSiQue上分别提升+13.6/+3.8分）和跨领域泛化（在多种问答任务上平均提升+9.3/+8.1分）方面均显著优于结果监督基线。我们的代码、数据和训练模型已公开，以促进未来研究。
英文摘要：
Recent advances in Large Language Models (LLMs) have highlighted the challenge of handling long-context tasks, where models need to reason over extensive input contexts to aggregate target information. While Chain-of-Thought (CoT) prompting has shown promise for multi-step reasoning, its effectiveness for long-context scenarios remains underexplored. Through systematic investigation across diverse tasks, we demonstrate that CoT's benefits generalize across most long-context scenarios and amplify with increasing context length. Motivated by this critical observation, we propose LongRePS, a process-supervised framework that teaches models to generate high-quality reasoning paths for enhanced long-context performance. Our framework incorporates a self-sampling mechanism to bootstrap reasoning paths and a novel quality assessment protocol specifically designed for long-context scenarios. Experimental results on various long-context benchmarks demonstrate the effectiveness of our approach, achieving significant improvements over outcome supervision baselines on both in-domain tasks (+13.6/+3.8 points for LLaMA/Qwen on MuSiQue) and cross-domain generalization (+9.3/+8.1 points on average across diverse QA tasks). Our code, data and trained models are made public to facilitate future research.
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。
