AgenticAI-DialogGen: Topic-Guided Conversation Generation for Fine-Tuning and Evaluating Short- and Long-Term Memories of LLMs

• URL: http://arxiv.org/abs/2604.12179v1
• Date: Tue, 14 Apr 2026 01:17:24 GMT
• Title: AgenticAI-DialogGen: Topic-Guided Conversation Generation for Fine-Tuning and Evaluating Short- and Long-Term Memories of LLMs
• Abstract summary: AgenticAI-DialogGen generates persona-grounded and topic-guided conversations without human supervision.<n>A new dataset entitled, TopicGuidedChat (TGC) encodes long-term memory as speaker-specific knowledge graphs and short-term memory as newly generated topic-guided conversations.
• Score: 10.842219538943356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Memory-Augmented Query Intent Understanding for Efficient Chat-based Image Retrieval [74.93546115702155]
  Chat-based image retrieval allows the human-interactive system to iteratively clarify and refine user intent through multi-round dialogue.<n>The key challenge in this task lies in dynamically understanding and updating the user's query intent across dialogue rounds.<n>This paper proposes a novel and efficient memory-based user intent updating framework for the chat-based image retrieval task.
  arXiv  Detail & Related papers  (2026-05-17T10:17:41Z)
• APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI [8.933188286750795]
  APEX-MEM is a conversational memory system that combines three key innovations.<n>Property graphs structure conversations as temporally grounded events in an entity-centric framework.<n>Retrieval agent understands and resolves conflicting or evolving information at query time, producing a compact and contextually relevant memory summary.
  arXiv  Detail & Related papers  (2026-04-15T19:25:02Z)
• PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments [72.02445514666428]
  We introduce PERMA, a benchmark designed to evaluate persona consistency over time beyond static preference recall.<n>PerMA consists of temporally ordered interaction events spanning multiple sessions and domains, with preference-related queries inserted over time.<n> Experiments demonstrate that by linking related interactions, advanced memory systems can extract more precise preferences and reduce token consumption.
  arXiv  Detail & Related papers  (2026-03-24T14:04:11Z)
• MMAG: Mixed Memory-Augmented Generation for Large Language Models Applications [0.0]
  Large Language Models (LLMs) excel at generating coherent text within a single prompt but fall short in sustaining relevance, personalization, and continuity across extended interactions.<n>This paper introduces the Mixed Memory-Augmented Generation (MMAG) pattern, a framework that organizes memory for LLM-based agents into five interacting layers.<n>We demonstrate the approach through its implementation in the Heero conversational agent, where long-term bios and conversational history already improve engagement and retention.
  arXiv  Detail & Related papers  (2025-12-01T14:16:57Z)
• SGMem: Sentence Graph Memory for Long-Term Conversational Agents [14.89396085814917]
  We introduce SGMem (Sentence Graph Memory), which represents dialogue as sentence-level graphs within chunked units.<n>We show that SGMem consistently improves accuracy and outperforms strong baselines in long-term conversational question answering.
  arXiv  Detail & Related papers  (2025-09-25T14:21:44Z)
• Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational History [13.389395397698035]
  We introduce a novel task named Memory-aware Proactive Dialogue (MapDia)<n>By the task, we then propose an automatic data construction method and create the first Chinese Memory-aware Proactive dataset (ChMapData)<n> Furthermore, we introduce a joint framework based on Retrieval Augmented Generation (RAG), featuring three modules: Topic Summarization, Topic Retrieval, and Proactive Topic-shifting Detection and Generation.
  arXiv  Detail & Related papers  (2025-03-07T05:19:17Z)
• Hello Again! LLM-powered Personalized Agent for Long-term Dialogue [63.65128176360345]
  We introduce a model-agnostic framework, the Long-term Dialogue Agent (LD-Agent)<n>It incorporates three independently tunable modules dedicated to event perception, persona extraction, and response generation.<n>The effectiveness, generality, and cross-domain capabilities of LD-Agent are empirically demonstrated.
  arXiv  Detail & Related papers  (2024-06-09T21:58:32Z)
• Evaluating Very Long-Term Conversational Memory of LLM Agents [95.84027826745609]
  We introduce a machine-human pipeline to generate high-quality, very long-term dialogues. We equip each agent with the capability of sharing and reacting to images. The generated conversations are verified and edited by human annotators for long-range consistency.
  arXiv  Detail & Related papers  (2024-02-27T18:42:31Z)
• Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models [30.48902594738911]
  Given a long conversation, large language models (LLMs) fail to recall past information and tend to generate inconsistent responses.<n>We propose to generate summaries/ memory using large language models (LLMs) to enhance long-term memory ability.
  arXiv  Detail & Related papers  (2023-08-29T04:59:53Z)
• AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models [74.10293412011455]
  We propose AutoConv for synthetic conversation generation. Specifically, we formulate the conversation generation problem as a language modeling task. We finetune an LLM with a few human conversations to capture the characteristics of the information-seeking process.
  arXiv  Detail & Related papers  (2023-08-12T08:52:40Z)
• History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System [59.78425104243993]
  We propose History-Aware Hierarchical Transformer (HAHT) for multi-session open-domain dialogue. HAHT maintains a long-term memory of history conversations and utilizes history information to understand current conversation context. Experimental results on a large-scale Multi-Session Conversation dataset suggest that the proposed HAHT model consistently outperforms baseline models.
  arXiv  Detail & Related papers  (2023-02-02T06:54:33Z)
• A Neural Conversation Generation Model via Equivalent Shared Memory Investigation [39.922967513749654]
  We propose a novel reading and memory framework called Deep Reading Memory Network (DRMN) DRMN is capable of remembering useful information of similar conversations for improving utterance generation. We apply our model to two large-scale conversation datasets of justice and e-commerce fields.
  arXiv  Detail & Related papers  (2021-08-20T13:20:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.