Open-domain Dialogue (OD) exhibits a one-to-many (o2m) property, whereby multiple appropriate responses exist for a single dialogue context. Despite prior research showing that modeling this property boosts response diversity, most modern LLM-based dialogue agents do not explicitly do so. In this work, we model the o2m property of OD in LLMs by decomposing OD generation into two key tasks: Multi-Response Generation (MRG) and Preference-based Selection (PS), which entail generating a set of n semantically and lexically diverse high-quality responses for a given dialogue context, followed by selecting a single response based on human preference, respectively. To facilitate MRG and PS, we introduce o2mDial, a dialogue corpus explicitly designed to capture the o2m property by featuring multiple plausible responses for each context. Leveraging o2mDial, we propose new in-context learning and instruction-tuning strategies, as well as novel evaluation metrics for MRG, alongside a model-based approach for PS. Empirical results demonstrate that applying the proposed two-stage framework to smaller LLMs for OD generation enhances overall response diversity while maintaining contextual coherence, improving response quality by up to 90%, bringing them closer to the performance of larger models.

翻译：开放域对话（OD）具有一对多（o2m）特性，即单个对话语境下存在多个合适的回应。尽管先前研究表明建模这一特性能够提升回应多样性，但当前大多数基于LLM的对话代理并未显式实现此建模。本研究通过将OD生成分解为两个关键任务——多回应生成（MRG）与基于偏好的选择（PS），在LLM中实现了OD的o2m特性建模。这两个任务分别指：针对给定对话语境生成包含n个语义与词汇层面均具多样性的高质量回应集合，以及依据人类偏好从中选择单一回应。为支持MRG与PS研究，我们构建了o2mDial对话语料库，该语料库通过为每个语境提供多个合理回应，显式捕捉了o2m特性。基于o2mDial，我们提出了新颖的上下文学习与指令微调策略，设计了针对MRG的评估指标，并建立了基于模型的PS方法。实验结果表明，将所提出的两阶段框架应用于较小规模LLM进行OD生成时，能在保持语境连贯性的同时显著提升整体回应多样性，使回应质量最高提升90%，使其性能更接近大规模模型。