Modeling the One-to-Many Property in Open-Domain Dialogue with LLMs

• URL: http://arxiv.org/abs/2506.15131v1
• Date: Wed, 18 Jun 2025 04:19:33 GMT
• Title: Modeling the One-to-Many Property in Open-Domain Dialogue with LLMs
• Authors: Jing Yang Lee, Kong-Aik Lee, Woon-Seng Gan,
• Abstract summary: Open-domain Dialogue (OD) exhibits a one-to-many (o2m) property, whereby multiple appropriate responses exist for a single dialogue context.<n>We model this property by decomposing OD generation into two key tasks: Multi-Response Generation (MRG) and Preference-based Selection (PS)<n> o2mDial is a dialogue corpus explicitly designed to capture the o2m property by featuring multiple plausible responses for each context.
• Score: 27.83533924583182
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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