  Recently, large language models (LLMs), such as GPT-4, stand out remarkable
conversational abilities, enabling them to engage in dynamic and contextually
relevant dialogues across a wide range of topics. However, given a long
conversation, these chatbots fail to recall past information and tend to
generate inconsistent responses. To address this, 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 chatbot can easily
generate a highly consistent response with the help of the latest memory. We
evaluate our method on both open and closed LLMs, and the experiments on the
widely-used public dataset show that our method can generate more consistent
responses in a long-context conversation. Also, we show that our strategy could
nicely complement both long-context (e.g., 8K and 16K) and retrieval-enhanced
LLMs, bringing further long-term dialogue performance. Notably, our method is a
potential solution to enable the LLM to model the extremely long context. The
code and scripts will be released later.
