Think-in-Memory: Recalling and Post-thinking Enable LLMs with Long-Term Memory
(2311.08719)Abstract
Memory-augmented LLMs have demonstrated remarkable performance in long-term human-machine interactions, which basically relies on iterative recalling and reasoning of history to generate high-quality responses. However, such repeated recall-reason steps easily produce biased thoughts, \textit{i.e.}, inconsistent reasoning results when recalling the same history for different questions. On the contrary, humans can keep thoughts in the memory and recall them without repeated reasoning. Motivated by this human capability, we propose a novel memory mechanism called TiM (Think-in-Memory) that enables LLMs to maintain an evolved memory for storing historical thoughts along the conversation stream. The TiM framework consists of two crucial stages: (1) before generating a response, a LLM agent recalls relevant thoughts from memory, and (2) after generating a response, the LLM agent post-thinks and incorporates both historical and new thoughts to update the memory. Thus, TiM can eliminate the issue of repeated reasoning by saving the post-thinking thoughts as the history. Besides, we formulate the basic principles to organize the thoughts in memory based on the well-established operations, (\textit{i.e.}, insert, forget, and merge operations), allowing for dynamic updates and evolution of the thoughts. Furthermore, we introduce Locality-Sensitive Hashing into TiM to achieve efficient retrieval for the long-term conversations. We conduct qualitative and quantitative experiments on real-world and simulated dialogues covering a wide range of topics, demonstrating that equipping existing LLMs with TiM significantly enhances their performance in generating responses for long-term interactions.
Overview
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The paper introduces Think-in-Memory (TiM), a novel framework for enhancing LLMs by incorporating a human-like memory mechanism, improving long-term interaction capabilities.
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TiM reduces biased or repetitive reasoning by enabling LLMs to recall historical thoughts and post-think, and incorporates operations like insert, forget, and merge for efficient memory management.
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The effectiveness of TiM is demonstrated through rigorous analysis, showing it enhances LLMs' long-term memory capabilities without needing significant architectural changes, and is applicable across various LLM architectures.
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Empirical results indicate that TiMs significantly outperform conventional memory mechanisms in tasks requiring long-term memory, such as medical dialogues, showcasing improved retrieval accuracy, response correctness, and contextual coherence.
Introduction
The continuous evolution of LLMs has significantly pushed the boundaries of AI's capabilities, showing exceptional performance in various domains ranging from finance to healthcare. However, their application in long-term human-machine interactions remains hampered due to LLMs' inherent limitation in handling prolonged conversational history effectively. In particular, the typical approach of iterative recalling and reasoning over historical data often leads to inconsistent outcomes and a high computational load. This paper introduces a novel framework, Think-in-Memory (TiM), designed to mitigate these limitations by incorporating a human-like memory mechanism into LLMs, thereby enhancing their long-term interaction capabilities.
The Think-in-Memory (TiM) Framework
TiM stands out by enabling LLMs to recall historical thoughts and post-think after generating a response, significantly reducing the tendency towards biased or repetitive reasoning. This two-stage framework not only facilitates the generation of more accurate and coherent responses but also dynamically updates memories to reflect new interactions and learnings. Importantly, TiM incorporates basic operations such as insert, forget, and merge to manage the memory efficiently, making it highly reflective of human cognitive processes.
Key Contributions
The paper makes bold claims about the effectiveness of TiM, backed by rigorous qualitative and quantitative analysis. By introducing principles for thought organization based on well-established cognitive processes and utilizing Locality-Sensitive Hashing for efficient memory retrieval, TiM sets a new precedent for enhancing LLMs' long-term memory capabilities. The method's LLM-agnostic nature further ensures wide applicability across various LLM architectures without necessitating significant modifications.
Numerical Results and Insights
The paper presents compelling numerical results demonstrating TiM's superiority over previous memory mechanisms. Across multiple datasets covering a broad range of topics and languages, LLMs equipped with TiM consistently outperform those using conventional memory mechanisms in terms of retrieval accuracy, response correctness, and contextual coherence. These improvements are particularly pronounced in real-world applications such as medical dialogues, where accurate, long-term memory recall is crucial for diagnosis accuracy and reliability.
Implications for the Future of AI Interactions
By addressing LLMs' limitations in long-term memory processing, TiM paves the way for more sophisticated and human-like AI agents capable of sustaining meaningful, multi-turn interactions across diverse domains. The framework's potential goes beyond enhancing existing LLMs to suggest a new design paradigm for future AI systems, focusing on cognitive accuracy and efficiency in long-term scenarios.
Conclusion
TiM represents a significant advancement in the field of LLMs, offering a viable solution to the challenge of enabling long-term memory in AI agents. Through a well-conceptualized framework and compelling experimental evidence, this paper highlights the importance of mirroring human-like memory mechanisms in AI systems to improve their interaction capabilities and effectiveness in real-world applications.