{"ID":2864998,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.02331","arxiv_id":"2510.02331","title":"Synthetic Dialogue Generation for Interactive Conversational Elicitation \u0026 Recommendation (ICER)","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.","short_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 uttera...","url_abs":"https://arxiv.org/abs/2510.02331","url_pdf":"https://arxiv.org/pdf/2510.02331v1","authors":"[\"Moonkyung Ryu\",\"Chih-Wei Hsu\",\"Yinlam Chow\",\"Mohammad Ghavamzadeh\",\"Craig Boutilier\"]","published":"2025-09-26T03:53:44Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\"]","methods":"[\"Language Model\"]","has_code":false}