Think-in-memory: Recalling And Post-thinking Enable Llms With Long-term Memory

Abstract

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 differ