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

• URL: http://arxiv.org/abs/2406.13167v2
• Date: Thu, 26 Sep 2024 09:42:48 GMT
• Title: QRMeM: Unleash the Length Limitation through Question then Reflection Memory Mechanism
• Authors: Bo Wang, Heyan Huang, Yixin Cao, Jiahao Ying, Wei Tang, Chong Feng,
• Abstract summary: Memory mechanism offers a flexible solution for managing long contexts. We introduce a novel strategy, Question then Reflection Memory Mechanism (QRMeM), incorporating a dual-structured memory pool. Our evaluation across multiple-choice questions (MCQ) and multi-document question answering (Multi-doc QA) benchmarks showcases QRMeM enhanced performance compared to existing approaches.
• Score: 46.441032033076034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization [50.485788083202124]
  Reinforcement Learning (RL) plays a crucial role in aligning large language models with human preferences and improving their ability to perform complex tasks. We introduce Direct Q-function Optimization (DQO), which formulates the response generation process as a Markov Decision Process (MDP) and utilizes the soft actor-critic (SAC) framework to optimize a Q-function directly parameterized by the language model. Experimental results on two math problem-solving datasets, GSM8K and MATH, demonstrate that DQO outperforms previous methods, establishing it as a promising offline reinforcement learning approach for aligning language models.
  arXiv  Detail & Related papers  (2024-10-11T23:29:20Z)
• Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding [28.191029786204624]
  We introduce the Long Question Coreference Adaptation (LQCA) method to enhance the performance of large language models (LLMs) This framework focuses on coreference resolution tailored to long contexts, allowing the model to identify and manage references effectively. The framework provides easier-to-handle partitions for LLMs, promoting better understanding.
  arXiv  Detail & Related papers  (2024-10-02T15:39:55Z)
• Do LLMs suffer from Multi-Party Hangover? A Diagnostic Approach to Addressee Recognition and Response Selection in Conversations [11.566214724241798]
  We propose a methodological pipeline to investigate model performance across specific structural attributes of conversations. We focus on Response Selection and Addressee Recognition tasks, to diagnose model weaknesses. Results show that response selection relies more on the textual content of conversations, while addressee recognition requires capturing their structural dimension.
  arXiv  Detail & Related papers  (2024-09-27T10:07:33Z)
• AQA: Adaptive Question Answering in a Society of LLMs via Contextual Multi-Armed Bandit [59.10281630985958]
  In question answering (QA), different questions can be effectively addressed with different answering strategies. We develop a dynamic method that adaptively selects the most suitable QA strategy for each question. Our experiments show that the proposed solution is viable for adaptive orchestration of a QA system with multiple modules.
  arXiv  Detail & Related papers  (2024-09-20T12:28:18Z)
• Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach [6.549143816134531]
  We propose a novel iterative RAG method called ReSP, equipped with a dual-function summarizer. Experimental results on the multi-hop question-answering HotpotQA and 2WikiMultihopQA demonstrate that our method significantly outperforms the state-of-the-art.
  arXiv  Detail & Related papers  (2024-07-18T02:19:00Z)
• Enhancing Textbook Question Answering Task with Large Language Models and Retrieval Augmented Generation [3.948068081583197]
  This paper proposes a methodology that handle the out-of-domain scenario in Textbook question answering (TQA) Through supervised fine-tuning of the LLM model Llama-2 and the incorporation of RAG, our architecture outperforms the baseline, achieving a 4.12% accuracy improvement on validation set and 9.84% on test set for non-diagram multiple-choice questions.
  arXiv  Detail & Related papers  (2024-02-05T11:58:56Z)
• Thread of Thought Unraveling Chaotic Contexts [133.24935874034782]
  "Thread of Thought" (ThoT) strategy draws inspiration from human cognitive processes. In experiments, ThoT significantly improves reasoning performance compared to other prompting techniques.
  arXiv  Detail & Related papers  (2023-11-15T06:54:44Z)
• Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading [63.93888816206071]
  We introduce MemWalker, a method that processes the long context into a tree of summary nodes. Upon receiving a query, the model navigates this tree in search of relevant information, and responds once it gathers sufficient information. We show that, beyond effective reading, MemWalker enhances explainability by highlighting the reasoning steps as it interactively reads the text; pinpointing the relevant text segments related to the query.
  arXiv  Detail & Related papers  (2023-10-08T06:18:14Z)
• RET-LLM: Towards a General Read-Write Memory for Large Language Models [53.288356721954514]
  RET-LLM is a novel framework that equips large language models with a general write-read memory unit. Inspired by Davidsonian semantics theory, we extract and save knowledge in the form of triplets. Our framework exhibits robust performance in handling temporal-based question answering tasks.
  arXiv  Detail & Related papers  (2023-05-23T17:53:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.