【第219期】AgenticLU：通过Chain-of-Clarifications提升模型长文本回答能力

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今天的主题是：

Self-Taught Agentic Long Context Understanding

Summary

The provided research paper introduces AgenticLU, a framework designed to improve how large language models understand and answer complex questions within long texts. This is achieved through a process called Chain-of-Clarifications (CoC), where the model asks itself clarifying questions and retrieves relevant context to enhance its comprehension. The framework trains the model using these self-generated reasoning paths to perform this clarification and retrieval efficiently in a single inference pass. Experimental results demonstrate that AgenticLU significantly outperforms existing methods on various long-context tasks by effectively utilizing information across extended inputs.

研究论文介绍了AgenticLU，一种旨在提升大型语言模型理解和回答长文本中复杂问题的框架。该框架通过“澄清链”（CoC）实现这一目标，即模型通过自问澄清问题并检索相关上下文来增强理解能力。该框架利用这些自生成的推理路径训练模型，使其在单次推理中高效执行澄清和检索。实验结果表明，AgenticLU在多种长上下文任务中显著优于现有方法，通过有效利用扩展输入中的信息。

原文链接：www.arxiv.org