Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactions-Reference-Cited by-同舟云学术

Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactions

Published:2024-03-04 Issue: Volume: Page:
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Container-title:Proceedings of the 17th ACM International Conference on Web Search and Data Mining
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Author:

Abbasiantaeb Zahra¹^ORCID,Yuan Yifei²^ORCID,Kanoulas Evangelos¹^ORCID,Aliannejadi Mohammad¹^ORCID

Affiliation:

1. University of Amsterdam, Amsterdam, Netherlands

2. The Chinese University of Hong Kong, Hong Kong, Hong Kong

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3616855.3635856

Reference56 articles.

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2. Jafar Afzali, Aleksander Mark Drzewiecki, Krisztian Balog, and Shuo Zhang. 2023. UserSimCRS: A User Simulation Toolkit for Evaluating Conversational Recommender Systems. arXiv abs/2301.05544 (2023).

3. Rachith Aiyappa, Jisun An, Haewoon Kwak, and Yong-Yeol Ahn. 2023. Can we trust the evaluation on ChatGPT? arXiv abs/2303.12767 (2023).

4. Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, and W. Bruce Croft. 2019. Asking Clarifying Questions in Open-Domain Information-Seeking Conversations. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval.

5. Arian Askari, Mohammad Aliannejadi, E. Kanoulas, and Suzan Verberne. 2023. Generating Synthetic Documents for Cross-Encoder Re-Rankers: A Comparative Study of ChatGPT and Human Experts. arXiv abs/2305.02320 (2023).

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