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Computer Science > Computation and Language

arXiv:2308.15022 (cs)
[Submitted on 29 Aug 2023]

Title:Recursively Summarizing Enables Long-Term Dialogue Memory in
Large Language Models

Authors:Qingyue Wang, Liang Ding, Yanan Cao, Zhiliang Tian, Shi Wang,
Dacheng Tao, Li Guo
Download a PDF of the paper titled Recursively Summarizing Enables
Long-Term Dialogue Memory in Large Language Models, by Qingyue Wang
and 6 other authors
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    Abstract: Most open-domain dialogue systems suffer from
    forgetting important information, especially in a long-term
    conversation. Existing works usually train the specific retriever
    or summarizer to obtain key information from the past, which is
    time-consuming and highly depends on the quality of labeled data.
    To alleviate this problem, we propose to recursively generate
    summaries/ memory using large language models (LLMs) to enhance
    long-term memory ability. Specifically, our method first
    stimulates LLMs to memorize small dialogue contexts and then
    recursively produce new memory using previous memory and
    following contexts. Finally, the LLM can easily generate a highly
    consistent response with the help of the latest memory. We
    evaluate our method using ChatGPT and text-davinci-003, and the
    experiments on the widely-used public dataset show that our
    method can generate more consistent responses in a long-context
    conversation. Notably, our method is a potential solution to
    enable the LLM to model the extremely long context. Code and
    scripts will be released later.

Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI)
Cite as:  arXiv:2308.15022 [cs.CL]
          (or arXiv:2308.15022v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2308.15022
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Qingyue Wang [view email]
[v1] Tue, 29 Aug 2023 04:59:53 UTC (425 KB)
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