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arXiv 2608.11200cs.CLcs.AIcs.LG

ConVAWG:面向针对妇女和女孩的暴力场景下可控合成对话生成的检索驱动框架

ConVAWG: A Retrieval-Grounded Framework for Controlled Synthetic Dialogue Generation in Violence Against Women and Girls

Chen Lyu, Xingwei Tan, Simon Cullen, Shelley Wilson, Lois Arthurs, Arshad Jhumka, Gabriele Pergola

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中文总结 AI 辅助

本研究针对VAWG场景建模的空白,提出检索驱动框架ConVAWG,生成符合CPS标准的合成VAWG多轮对话,发布6000余个对话事件,经多类评估验证其质量与领域保真度。

中文摘要 AI 辅助

合成对话生成为研究敏感领域的对话动态提供了途径,在这些领域中,真实数据难以获取、发布或标注。潜在的虐待可能发生在线上或线下:威胁和胁迫可直接出现在消息中,而监视、孤立、跟踪和身体暴力等行为可能被计划、披露或通过对话提及。隐私和法律约束使得发布大规模真实对话数据集变得困难;现有工作大多聚焦于线上虐待的句子级毒性,而在将虐待建模为一种关系性且随时间展开的现象方面存在空白。本研究将针对妇女和女孩的暴力(VAWG)场景建模为多轮对话。我们提出ConVAWG,这是一个用于生成符合CPS标准的合成VAWG聊天对话的检索驱动框架。ConVAWG基于角色种子、英国国家统计局报告的人口统计模式、官方犯罪定义以及检索到的家庭凶杀案审查案例构建场景;将其转换为分层事件时间线;生成多场景角色扮演对话;并对适当的话语应用针对性的激活引导毒性控制。我们发布了涵盖200个场景的6000多个多轮对话事件,带有丰富的场景级、事件级和轮级元数据。广泛的人工评估、大模型作为评判者的评估、消融实验以及下游任务表明,该框架具有出色的对话质量和领域保真度。

英文摘要

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.

发表机构

  • University of Sheffield(谢菲尔德大学)
  • Forensic Capability Network(法医能力网络)
  • University of Leeds(利兹大学)
  • University of Warwick(华威大学)

机构由 AI 辅助整理,请以论文原文为准。

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