Synthetic Dialogue Generation for Interactive Conversational Elicitation & Recommendation (ICER)

• URL: http://arxiv.org/abs/2510.02331v1
• Date: Fri, 26 Sep 2025 03:53:44 GMT
• Title: Synthetic Dialogue Generation for Interactive Conversational Elicitation & Recommendation (ICER)
• Authors: Moonkyung Ryu, Chih-Wei Hsu, Yinlam Chow, Mohammad Ghavamzadeh, Craig Boutilier,
• Abstract summary: We develop a methodology for generating natural dialogues consistent with a user's underlying state using behavior simulators together with LM-prompting.<n>We illustrate our approach by generating a large, open-source CRS data set with both preference elicitation and example critiquing.
• Score: 29.20001042457133
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While language models (LMs) offer great potential for conversational recommender systems (CRSs), the paucity of public CRS data makes fine-tuning LMs for CRSs challenging. In response, LMs as user simulators qua data generators can be used to train LM-based CRSs, but often lack behavioral consistency, generating utterance sequences inconsistent with those of any real user. To address this, we develop a methodology for generating natural dialogues that are consistent with a user's underlying state using behavior simulators together with LM-prompting. We illustrate our approach by generating a large, open-source CRS data set with both preference elicitation and example critiquing. Rater evaluation on some of these dialogues shows them to exhibit considerable consistency, factuality and naturalness.

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