Hello Again! LLM-powered Personalized Agent for Long-term Dialogue

ACL ARR 2024 August Submission134 Authors

14 Aug 2024 (modified: 04 Sept 2024)ACL ARR 2024 August SubmissionEveryoneRevisionsBibTeXCC BY 4.0
Abstract:

Open-domain dialogue systems have seen remarkable advancements with the development of large language models (LLMs). Nonetheless, most existing dialogue systems predominantly focus on brief single-session interactions, neglecting the real-world demands for long-term companionship and personalized interactions with chatbots. Crucial to addressing this real-world need are event summary and persona management, which enable reasoning for appropriate long-term dialogue responses. Recent progress in the human-like cognitive and reasoning capabilities of LLMs suggests that LLM-based agents could significantly enhance automated perception, decision-making, and problem-solving. In response to this potential, we introduce a model-agnostic framework, the Long-term Dialogue Agent (LD-Agent), which incorporates three independently tunable modules dedicated to event perception, persona extraction, and response generation. For the event memory module, long and short-term memory banks are employed to separately focus on historical and ongoing sessions, while a topic-based retrieval mechanism is introduced to enhance the accuracy of memory retrieval. Furthermore, the persona module conducts dynamic persona modeling for both users and agents. The integration of retrieved memories and extracted personas is subsequently fed into the generator to induce appropriate responses. The effectiveness, generality, and cross-domain capabilities of LD-Agent are empirically demonstrated across various illustrative benchmarks, models, and tasks. The code is released at https://anonymous.4open.science/r/LDA-D7B6.

Paper Type: Long
Research Area: Dialogue and Interactive Systems
Research Area Keywords: long-term dialogue, open-domain dialogue, autonomous agent, LLM-based agent
Contribution Types: NLP engineering experiment
Languages Studied: English
Submission Number: 134
Loading

OpenReview is a long-term project to advance science through improved peer review with legal nonprofit status. We gratefully acknowledge the support of the OpenReview Sponsors. © 2025 OpenReview