@inproceedings{duan-etal-2025-guidellm,
    title = "{G}uide{LLM}: Exploring {LLM}-Guided Conversation with Applications in Autobiography Interviewing",
    author = "Duan, Jinhao  and
      Zhao, Xinyu  and
      Zhang, Zhuoxuan  and
      Ko, Eunhye Grace  and
      Boddy, Lily  and
      Wang, Chenan  and
      Li, Tianhao  and
      Rasgon, Alexander  and
      Hong, Junyuan  and
      Lee, Min Kyung  and
      Yuan, Chenxi  and
      Long, Qi  and
      Ding, Ying  and
      Chen, Tianlong  and
      Xu, Kaidi",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.287/",
    pages = "5558--5588",
    ISBN = "979-8-89176-189-6",
    abstract = "Although Large Language Models (LLMs) succeed in human-guided conversations such as instruction following and question answering, the potential of LLM-guided conversations{---}where LLMs direct the discourse and steer the conversation{'}s objectives{---}remains under-explored. In this study, we first characterize LLM-guided conversation into three fundamental components: (i) Goal Navigation; (ii) Context Management; (iii) Empathetic Engagement, and propose GuideLLM as an installation. We then implement an interviewing environment for the evaluation of LLM-guided conversation. Specifically, various topics are involved in this environment for comprehensive interviewing evaluation, resulting in around 1.4k turns of utterances, 184k tokens, and over 200 events mentioned during the interviewing for each chatbot evaluation. We compare GuideLLM with 6 state-of-the-art LLMs such as GPT-4o and Llama-3-70b-Instruct, from the perspective of interviewing quality, and autobiography generation quality. For automatic evaluation, we derive user proxies from multiple autobiographies and employ LLM-as-a-judge to score LLM behaviors. We further conduct a human-involved experiment by employing 45 human participants to chat with GuideLLM and baselines. We then collect human feedback, preferences, and ratings regarding the qualities of conversation and autobiography. Experimental results indicate that GuideLLM significantly outperforms baseline LLMs in automatic evaluation and achieves consistent leading performances in human ratings."
}