Synthetic Dialogue Generation for Interactive Conversational Elicitation & Recommendation (ICER)
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909821585850368 |
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| author | Ryu, Moonkyung Hsu, Chih-Wei Chow, Yinlam Ghavamzadeh, Mohammad Boutilier, Craig |
| author_facet | Ryu, Moonkyung Hsu, Chih-Wei Chow, Yinlam Ghavamzadeh, Mohammad Boutilier, Craig |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02331 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Synthetic Dialogue Generation for Interactive Conversational Elicitation & Recommendation (ICER) Ryu, Moonkyung Hsu, Chih-Wei Chow, Yinlam Ghavamzadeh, Mohammad Boutilier, Craig Computation and Language Artificial Intelligence 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. |
| title | Synthetic Dialogue Generation for Interactive Conversational Elicitation & Recommendation (ICER) |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2510.02331 |