再次见面！基于大语言模型（LLM）的长期对话个性化智能体
Hello Again! LLM-powered Personalized Agent for Long-term Dialogue
AI总结：
针对现有对话系统忽视长期陪伴与个性化交互需求的问题，本文提出模型无关的长期对话智能体（LD-Agent）框架，通过事件感知、人物角色提取及响应生成模块实现长期对话能力，经多基准验证其有效性、通用性与跨域能力。
摘要：
随着大语言模型（LLMs）的发展，开放域对话系统取得了显著进步。然而，大多数现有对话系统主要聚焦于简短的单轮会话交互，忽视了现实世界中对聊天机器人长期陪伴和个性化交互的需求。满足这一现实需求的关键在于事件摘要和人物角色管理，它们能为生成合适的长期对话响应提供推理依据。LLMs在类人认知和推理能力方面的最新进展表明，基于LLM的智能体可显著增强自动化感知、决策和问题解决能力。针对这一潜力，我们提出了一个模型无关的框架——长期对话智能体（LD-Agent），该框架包含三个独立可调模块，分别用于事件感知、人物角色提取和响应生成。事件记忆模块采用长短期记忆库，分别聚焦历史会话和当前会话，同时引入基于主题的检索机制以提升记忆检索的准确性。此外，人物角色模块对用户和智能体进行动态人物角色建模。检索到的记忆和提取的人物角色随后被输入生成器，以诱导合适的响应。通过在多个示例基准、模型和任务上的实证验证，LD-Agent的有效性、通用性和跨域能力得到了证明。代码已在https://github.com/leolee99/LD-Agent发布。
英文摘要：
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://github.com/leolee99/LD-Agent.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
