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MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation

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arxiv 2308.08239 v2 pith:D5PE6RTD submitted 2023-08-16 cs.CL

Junru Lu , Siyu An , Mingbao Lin , Gabriele Pergola , Yulan He , Di Yin , Xing Sun , Yunsheng Wu This is my paper · ORCID
classification cs.CL
keywords long-rangememochatconversationsinstructionsllmsmemosopen-domainconsistency
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Forward citations

Cited by 12 Pith papers

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