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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. 

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 largely untapped. In this study, we provide an exploration of the LLM-guided conversation paradigm. Specifically, we first characterize LLM-guided conversation into three fundamental properties: (i) Goal Navigation; (ii) Context Management; (iii) Empathetic Engagement, and propose GUIDELLM as a general framework for LLM-guided conversation. We then implement an autobiography interviewing environment as one of the demonstrations of GuideLLM, which is a common practice in Reminiscence Therapy. In this environment, various techniques are integrated with GUIDELLM to enhance the autonomy of LLMs, such as Verbalized Interview Protocol (VIP) and Memory Graph Extrapolation (MGE) for goal navigation, and therapy strategies for empathetic engagement. We compare GUIDELLM with baseline LLMs, such as GPT-4-turbo and GPT-4o, from the perspective of interviewing quality, conversation quality, and autobiography generation quality. Experimental results encompassing both LLM-as-a-judge evaluations and human subject experiments involving 45 participants indicate that GUIDELLM significantly outperforms baseline LLMs in the autobiography interviewing task.