用行为模拟器生成真实用户风格的对话数据，解决推荐系统训练数据不足问题。
Synthetic Dialogue Generation for Interactive Conversational Elicitation & Recommendation (ICER)
- 通过行为模拟器+大模型提示生成符合用户状态的自然对话
- 构建大规模开源对话数据集，支持偏好挖掘与评论反馈
- 人工评估显示对话具高一致性、事实性和自然度，适合推荐系统训练
尽管语言模型在对话推荐系统中潜力巨大，但公开的对话推荐数据稀缺，制约了其微调应用。为此，本文提出一种方法：利用语言模型作为用户模拟器生成数据，结合行为模拟器与提示工程，生成与用户潜在状态一致的自然对话。我们基于此构建了一个大规模、开源的对话推荐数据集，涵盖偏好获取与示例批评两类任务。人工评估表明，部分生成对话在一致性、事实性和自然度方面表现良好，具备实际可用性。
原文摘要 · Abstract (English)
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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