Using Large Language Models for Adaptive Dialogue Management in Digital Telephone Assistants-Reference-Cited by-同舟云学术

Using Large Language Models for Adaptive Dialogue Management in Digital Telephone Assistants

Published:2024-06-27 Issue: Volume: Page:399-405
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Container-title:Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization
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Author:

Soliman Hassan¹^ORCID,Kravcik Milos¹^ORCID,Basvoju Nagasandeepa¹^ORCID,Jähnichen Patrick²^ORCID

Affiliation:

1. Educational Technology Lab, DFKI GmbH, Germany

2. Aaron GmbH, Germany

Funder

German Federal Ministry of Education and Research (BMBF)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3631700.3664902

Reference16 articles.

1. Medbot: Conversational Artificial Intelligence Powered Chatbot for Delivering Tele-Health after COVID-19

2. Sarah E Finch and Jinho D Choi. 2020. Towards unified dialogue system evaluation: A comprehensive analysis of current evaluation protocols. arXiv preprint arXiv:2006.06110 (2020).

3. Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots

4. Dan Hendrycks Collin Burns Steven Basart Andy Zou Mantas Mazeika Dawn Song and Jacob Steinhardt. 2021. Measuring Massive Multitask Language Understanding. arxiv:2009.03300 [cs.CY]

5. Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulators to Enhance Dialogue System

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