SeCom: On Memory Construction and Retrieval for Personalized Conversational Agents

Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang, Xufang Luo, Hao Cheng, Dongsheng Li, Yuqing Yang, Chin-Yew Lin, H. Vicky Zhao, Lili Qiu, Jianfeng Gao

Keywords: memory management, conversational agent, RAG, text segmentation, prompt compression

TL;DR: A system facilitates long-term conversational agents by constructing a memory bank at segment level while applying compression-based denoising to enhance memory retrieval.

Abstract:

To deliver coherent and personalized experiences in long-term conversations, existing approaches typically perform retrieval augmented response generation by constructing memory banks from conversation history at either the turn-level, session-level, or through summarization techniques. In this paper, we explore the impact of different memory granularities and present two key findings: (1) Both turn-level and session-level memory units are suboptimal, affecting not only the quality of final responses, but also the accuracy of the retrieval process. (2) The redundancy in natural language introduces noise, hindering precise retrieval. We demonstrate that LLMLingua-2, originally designed for prompt compression to accelerate LLM inference, can serve as an effective denoising method to enhance memory retrieval accuracy.

Building on these insights, we propose SeCom, a method that constructs a memory bank with topical segments by introducing a conversation Segmentation model, while performing memory retrieval based on Compressed memory units. Experimental results show that SeCom outperforms turn-level, session-level, and several summarization-based methods on long-term conversation benchmarks such as LOCOMO and Long-MT-Bench+. Additionally, the proposed conversation segmentation method demonstrates superior performance on dialogue segmentation datasets such as DialSeg711, TIAGE, and SuperDialSeg.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 9546