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

• URL: http://arxiv.org/abs/2406.05925v2
• Date: Thu, 13 Feb 2025 18:02:34 GMT
• Title: Hello Again! LLM-powered Personalized Agent for Long-term Dialogue
• Authors: Hao Li, Chenghao Yang, An Zhang, Yang Deng, Xiang Wang, Tat-Seng Chua,
• Abstract summary: We introduce a model-agnostic framework, the Long-term Dialogue Agent (LD-Agent) It incorporates three independently tunable modules dedicated to event perception, persona extraction, and response generation. The effectiveness, generality, and cross-domain capabilities of LD-Agent are empirically demonstrated.
• Score: 63.65128176360345
• License:
• 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://github.com/leolee99/LD-Agent.

Related papers

• REALTALK: A 21-Day Real-World Dataset for Long-Term Conversation [51.97224538045096]
  We introduce REALTALK, a 21-day corpus of authentic messaging app dialogues. We compare EI attributes and persona consistency to understand the challenges posed by real-world dialogues. Our findings reveal that models struggle to simulate a user solely from dialogue history, while fine-tuning on specific user chats improves persona emulation.
  arXiv  Detail & Related papers  (2025-02-18T20:29:01Z)
• InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System for Long-term Streaming Video and Audio Interactions [104.90258030688256]
  This project introduces disentangled streaming perception, reasoning, and memory mechanisms, enabling real-time interaction with streaming video and audio input. This project simulates human-like cognition, enabling multimodal large language models to provide continuous and adaptive service over time.
  arXiv  Detail & Related papers  (2024-12-12T18:58:30Z)
• A Survey on the Memory Mechanism of Large Language Model based Agents [66.4963345269611]
  Large language model (LLM) based agents have recently attracted much attention from the research and industry communities. LLM-based agents are featured in their self-evolving capability, which is the basis for solving real-world problems. The key component to support agent-environment interactions is the memory of the agents.
  arXiv  Detail & Related papers  (2024-04-21T01:49:46Z)
• 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)
• Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management [36.254564021059515]
  Reinforcement learning (RL) has shown great promise for developing dialogue management (DM) agents that are non-myopic. We develop a variety of RL algorithms, specialized to dialogue planning, that leverage recent Mixture-of-Expert Language Models (MoE-LMs) By exploiting MoE-LM structure, our methods significantly reduce the size of the action space and improve the efficacy of RL-based DM.
  arXiv  Detail & Related papers  (2023-02-21T18:02:20Z)
• A Mixture-of-Expert Approach to RL-based Dialogue Management [56.08449336469477]
  We use reinforcement learning to develop a dialogue agent that avoids being short-sighted (outputting generic utterances) and maximizes overall user satisfaction. Most existing RL approaches to DM train the agent at the word-level, and thus, have to deal with aly complex action space even for a medium-size vocabulary. We develop a RL-based DM using a novel mixture of expert language model (MoE-LM) that consists of (i) a LM capable of learning diverse semantics for conversation histories, (ii) a number of specialized LMs (or experts) capable of generating utterances corresponding to a
  arXiv  Detail & Related papers  (2022-05-31T19:00:41Z)
• Long Time No See! Open-Domain Conversation with Long-Term Persona Memory [37.51131984324123]
  We present a novel task of Long-term Memory Conversation (LeMon) We then build a new dialogue dataset DuLeMon and a dialogue generation framework with Long-Term Memory (LTM) mechanism. Results on DuLeMon indicate that PLATO-LTM can significantly outperform baselines in terms of long-term dialogue consistency.
  arXiv  Detail & Related papers  (2022-03-11T08:41:14Z)

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.