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QRMeM: Unleash the Length Limitation through Question then Reflection Memory Mechanism

2024-06-19 02:46:18
Bo Wang, Heyan Huang, Yixin Cao, Jiahao Ying, Wei Tang, Chong Feng

Abstract

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.

Abstract (translated)

尽管大型语言模型(LLMs)在自然语言处理方面取得了显著的进步,但它们仍然难以处理大量文本。记忆机制提供了一个灵活的解决方案,用于管理长文本,利用压缩、总结和结构化等技术促进对大量文本的微妙和高效的处理。然而,现有的技术面临静态知识整合的挑战,导致对任务特定需求不足的适应,并缺失多段关系,这阻碍了在响应过程中对相关部分的动态重组和逻辑组合。为解决这些问题,我们引入了一种名为问题然后反思记忆机制(QRMeM)的新策略,包括一个双层结构的记忆池。这个池将静态文本内容与结构化图指导相结合,推动了一种反思式的尝试和错误方法,用于导航和确定相关段落。我们在多选题(MCQ)和多文档问题回答(Multi-doc QA)基准测试中评估了QRMeM与现有方法的性能,展示了QRMeM相较于现有方法增强了性能。

URL

https://arxiv.org/abs/2406.13167

PDF

https://arxiv.org/pdf/2406.13167.pdf


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