Memory-augmented Large Language Models (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.

提出了一种称为 TiM（Think-in-Memory）的新型记忆机制，通过保存后思考的思想作为历史记录，使得 LLMs 能够维护进化的记忆以存储历史思想，并且通过引入局部敏感哈希实现了对长期对话的高效检索，从而显著提升了现有 LLMs 在生成长期互动响应方面的性能。