[2312.13925] AsyncMLD: Asynchronous Multi-LLM Framework for Dialogue Recommendation System
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Computer Science > Human-Computer Interaction

arXiv:2312.13925 (cs)
[Submitted on 21 Dec 2023]

Title:AsyncMLD: Asynchronous Multi-LLM Framework for Dialogue Recommendation System

Authors:Naoki Yoshimaru, Motoharu Okuma, Takamasa Iio, Kenji Hatano
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Abstract:We have reached a practical and realistic phase in human-support dialogue agents by developing a large language model (LLM). However, when requiring expert knowledge or anticipating the utterance content using the massive size of the dialogue database, we still need help with the utterance content's effectiveness and the efficiency of its output speed, even if using LLM. Therefore, we propose a framework that uses LLM asynchronously in the part of the system that returns an appropriate response and in the part that understands the user's intention and searches the database. In particular, noting that it takes time for the robot to speak, threading related to database searches is performed while the robot is speaking.
Comments: This paper is part of the proceedings of the Dialogue Robot Competition 2023
Subjects: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Robotics (cs.RO)
Cite as: arXiv:2312.13925 [cs.HC]
  (or arXiv:2312.13925v1 [cs.HC] for this version)
  https://doi.org/10.48550/arXiv.2312.13925

Submission history

From: Yoshimaru Naoki [view email]
[v1] Thu, 21 Dec 2023 15:12:59 UTC (375 KB)
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