想研究敏感场景对话生成？ConVAWG 用检索增强和毒性控制，造出 6000 多轮高质量对话，还带详细元数据，值得一看。
ConVAWG 是一个用于生成暴力侵害女性（VAWG）场景多轮对话的检索增强框架。它利用英国国家统计局的人口统计数据、官方犯罪定义和家庭凶杀案审查案例构建场景，并生成超过 6,000 个多轮对话事件，覆盖 200 个场景。框架采用分层事件时间线生成多场景角色扮演对话，并通过激活导向的毒性控制调整不当表述。人工评估和 LLM-as-Judge 评估显示其对话质量和领域保真度较高。该研究旨在解决敏感领域真实对话数据难以获取的问题。
ConVAWG: A Retrieval-Grounded Framework for Controlled Synthetic Dialogue Generation in Violence Against Women and Girls
Synthetic dialogue generation offers a way to study conversational dynamics in sensitive domains where real data are difficult to access, release, or annotate. The underlying abuse may occur online or offline: threats and coercion can appear directly in messages, while behaviours such as surveillance, isolation, stalking, and physical violence may be planned, disclosed, or referred to conversationally. Privacy and legal constraints make it difficult the release of large-scale real conversation datasets; existing work has mostly focused on sentence-level toxicity of online abuses, leaving a gap in modelling abuse as a relational and temporally unfolding phenomenon. In this work, we focus on modelling Violence Against Women and Girls (VAWG) scenarios as multi-turn dialogues. We introduce ConVAWG, a retrieval-grounded framework for generating CPS-aligned synthetic VAWG chat dialogues. ConVAWG builds scenarios from persona seeds, demographic patterns reported by the UK Office for National Statistics, official crime definitions, and retrieved Domestic Homicide Review cases; converts them into hierarchical event timelines; generates multi-scene role-play dialogues; and applies targeted activation-steered toxicity control to appropriate utterances. We release over 6,000 multi-turn dialogue events across 200 scenarios with rich scenario-, event-, and turn-level metadata. Extensive human evaluation, LLM-as-Judge assessment, ablations, and downstream tasks show strong dialogue quality and domain fidelity.