用扩散模型做风格迁移，生成更公平的阿拉伯语心理健康文本。
Style Transfer as Bias Mitigation: Diffusion Models for Synthetic Mental Health Text for Arabic
- 将偏见缓解视为风格迁移问题，不用预训练大模型。
- 生成文本语义准确率高，性别风格差异明显。
- 适合低资源、敏感领域中的公平性研究者使用。
合成数据为缓解心理健康分析中的数据稀缺和人口偏差提供了可行方案，但现有方法多依赖预训练大语言模型（LLMs），存在输出多样性不足及继承训练数据偏见的问题。本文提出一种无需预训练的扩散模型方法，将偏见缓解视为风格迁移任务。基于具有显著性别失衡的CARMA阿拉伯语心理健康语料库，聚焦从男性到女性的风格迁移以增强代表性不足的女性文本。构建了五个涵盖阿拉伯语性别表达不同语言与语义层面的数据集，并为每种设定训练独立扩散模型。定量评估显示源文本与生成文本间保持高度语义保真度，同时表面风格有显著差异；定性分析证实生成内容在语言上合理且实现性别转换。结果表明，该方法可在不依赖LLMs的前提下生成高熵、语义忠实的合成数据，为敏感、低资源的心理健康领域提供有效且灵活的性别偏见缓解框架。
原文摘要 · Abstract (English)
Synthetic data offers a promising solution for mitigating data scarcity and demographic bias in mental health analysis, yet existing approaches largely rely on pretrained large language models (LLMs), which may suffer from limited output diversity and propagate biases inherited from their training data. In this work, we propose a pretraining-free diffusion-based approach for synthetic text generation that frames bias mitigation as a style transfer problem. Using the CARMA Arabic mental health corpus, which exhibits a substantial gender imbalance, we focus on male-to-female style transfer to augment underrepresented female-authored content. We construct five datasets capturing varying linguistic and semantic aspects of gender expression in Arabic and train separate diffusion models for each setting. Quantitative evaluations demonstrate consistently high semantic fidelity between source and generated text, alongside meaningful surface-level stylistic divergence, while qualitative analysis confirms linguistically plausible gender transformations. Our results show that diffusion-based style transfer can generate high-entropy, semantically faithful synthetic data without reliance on pretrained LLMs, providing an effective and flexible framework for mitigating gender bias in sensitive, low-resource mental health domains.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。