MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation
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Cited by 50 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
We propose MemoChat, a pipeline for refining instructions that enables large language models (LLMs) to effectively employ self-composed memos for maintaining consistent long-range open-domain conversations. We demonstrate a long-range open-domain conversation through iterative "memorization-retrieval-response" cycles. This requires us to carefully design tailored tuning instructions for each distinct stage. The instructions are reconstructed from a collection of public datasets to teach the LLMs to memorize and retrieve past dialogues with structured memos, leading to enhanced consistency when participating in future conversations. We invite experts to manually annotate a test set designed to evaluate the consistency of long-range conversations questions. Experiments on three testing scenarios involving both open-source and API-accessible chatbots at scale verify the efficacy of MemoChat, which outperforms strong baselines. Our codes, data and models are available here: https://github.com/LuJunru/MemoChat.
Key facts. Cited by 94 papers in the map (data through 7 Oct 2026). Builds on 54 of its 68 references that are in the map. First posted on arXiv 16 Aug 2023.
Builds on
54 of 68 references.
References · 54
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