While large language models (LLMs) have made notable advancements in natural language processing, they continue to struggle with processing extensive text. Memory mechanism offers a flexible solution for managing long contexts, utilizing techniques such as compression, summarization, and structuring to facilitate nuanced and efficient handling of large volumes of text. However, existing techniques face challenges with static knowledge integration, leading to insufficient adaptation to task-specific needs and missing multi-segmentation relationships, which hinders the dynamic reorganization and logical combination of relevant segments during the response process. To address these issues, we introduce a novel strategy, Question then Reflection Memory Mechanism (QRMeM), incorporating a dual-structured memory pool. This pool synergizes static textual content with structured graph guidance, fostering a reflective trial-and-error approach for navigating and identifying relevant segments. Our evaluation across multiple-choice questions (MCQ) and multi-document question answering (Multi-doc QA) benchmarks showcases QRMeM enhanced performance compared to existing approaches.

翻译：尽管大型语言模型（LLMs）在自然语言处理领域取得了显著进展，其在处理长文本方面仍面临挑战。记忆机制通过压缩、摘要和结构化等技术，为管理长上下文提供了灵活的解决方案，有助于实现对大容量文本的精细且高效处理。然而，现有技术面临静态知识整合的挑战，导致对任务特定需求的适应性不足，且缺乏多片段关联性，从而在响应过程中阻碍了相关片段的动态重组与逻辑整合。为解决这些问题，我们提出了一种新颖的策略——先提问后反思记忆机制（QRMeM），该机制融合了双结构记忆池。该记忆池将静态文本内容与结构化图引导相结合，通过反思试错的方式促进对相关片段的导航与识别。我们在多项选择题（MCQ）和多文档问答（Multi-doc QA）基准测试上的评估表明，QRMeM相较于现有方法实现了性能提升。