AgenticAI-DialogGen：基于主题引导的对话生成用于微调和评估LLM的短期和长期记忆
AgenticAI-DialogGen: Topic-Guided Conversation Generation for Fine-Tuning and Evaluating Short- and Long-Term Memories of LLMs
AI总结：
本文提出AgenticAI-DialogGen框架，通过生成基于角色和主题的对话来微调和评估LLM的短期和长期记忆，引入了TopicGuidedChat数据集以提升对话质量和模型性能。
摘要：
近年来，大型语言模型（LLM）在处理扩展对话上下文方面取得了进展，但微调和评估短期和长期记忆仍困难，因为缺乏同时编码短期和长期对话历史的数据集。现有对话数据集缺乏记忆基础、忽视主题连续性或依赖昂贵的人工标注。为解决这些差距，我们引入了AgenticAI-DialogGen，一个模块化的代理框架，能够生成基于角色和主题引导的对话，无需人工监督。该框架利用LLM代理提取知识图谱、识别主题、构建说话者角色，并从无结构对话中模拟主题引导的对话。一个问答模块生成基于记忆的问答对，来源于短期和长期对话历史。我们还生成了一个名为TopicGuidedChat（TGC）的新数据集，其中长期记忆编码为说话者特定的知识图谱，短期记忆则为新生成的主题引导对话。评估显示，AgenticAI-DialogGen产生了更高的对话质量，且在TGC数据集上微调的LLM在基于记忆的问答任务中表现更佳。
英文摘要：
Recent advancements in Large Language Models (LLMs) have improved their ability to process extended conversational contexts, yet fine-tuning and evaluating short- and long-term memories remain difficult due to the absence of datasets that encode both short- and long-term conversational history. Existing conversational datasets lack memory grounding, overlook topic continuity, or rely on costly human annotation. To address these gaps, we introduce AgenticAI-DialogGen, a modular agent-based framework that generates persona-grounded and topic-guided conversations without human supervision. The framework uses LLM agents to extract knowledge graphs, identify topics, build speaker personas, and simulate topic-guided conversations from unstructured conversations. A QA module generates memory-grounded Question Answer (QA) pairs drawn from short- and long-term conversational histories. We also generated a new dataset entitled, TopicGuidedChat (TGC), where long-term memory is encoded as speaker-specific knowledge graphs and short-term memory as newly generated topic-guided conversations. Evaluations depict that AgenticAI-DialogGen yields higher conversational quality and LLMs fine-tuned on TGC dataset achieve improved performance on memory-grounded QA tasks.
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。
