Extracting User Needs With Chat-GPT for Dialogue Recommendation: Conclusion & References by@textmodels
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Extracting User Needs With Chat-GPT for Dialogue Recommendation: Conclusion & References
by Writings, Papers and Blogs on Text ModelsFebruary 8th, 2024
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Explore the conclusion of the study verifying Chat-GPT's efficacy in managing dynamic dialogues for user needs extraction. Understand how the system dynamically controls dialogues, inferring user preferences effectively, and its potential future applications.
Authors:
(1) Yugen Sato, Meiji University;
(2) Taisei Nakajima, Meiji University;
(3) Tatsuki Kawamoto, Meiji University;
(4) Tomohiro Takagi, Meiji University.
Table of Links
5 Conclusion
We verified Chat-GPT’s capabilities and effectiveness in managing dynamic dialogues for the purpose of extracting user needs by simulating dialogues between a controller and an assistant using Chat-GPT. As a result, we confirmed that our system is capable of dynamically controlling the dialogue while inferring user preferences and requests, and forming dialogue scenarios that are highly satisfactory for the user. We expect that Chat-GPT’s dialogue management capabilities will be used in the future for applications that are truly valuable to users.
References
[1] Lang Cao. DiagGPT: An LLM-based Chatbot with Automatic Topic Management for Task-Oriented Dialogue. 2023. arXiv: 2308.08043 [cs.CL].
[2] Yang Liu et al. G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment. 2023. arXiv: 2303.16634 [cs.CL].
[3] Open AI,Chat-GPT. https://openai.com/blog/chatgpt.
[4] Open AI,Chat-GPT. https://chat.openai.com/.
[5] OpenAI. “GPT-4 Technical Report”. In: ArXiv abs/2303.08774 (2023).
[6] Jason Wei et al. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. 2023. arXiv: 2201. 11903 [cs.CL].
This paper is available on arxiv under CC 4.0 license.
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