使用大型语言模型进行基于多阶段条件化的人格合成数据生成以用于情感识别
Persona-Based Synthetic Data Generation Using Multi-Stage Conditioning with Large Language Models for Emotion Recognition
AI总结：
针对情感数据集稀缺问题，提出基于多阶段人格条件化的大型语言模型框架PersonaGen，通过构建分层虚拟人生成高质量情感文本，在多样性、真实性与下游任务中均显著优于基线。
摘要：
在情感识别领域，由于缺乏高质量、多样化的情感数据集，开发高性能模型仍然是一项挑战。情感表达本质上是主观的，受个体性格特征、社会文化背景和情境因素影响，使得大规模、可泛化的数据收集在伦理和实践上都很困难。为了解决这个问题，我们引入了PersonaGen，这是一种通过基于多阶段人格条件化使用大型语言模型（LLM）生成情感丰富文本的新颖框架。PersonaGen通过结合人口统计属性、社会文化背景和详细的情境上下文构建分层虚拟人格，然后用于指导情感表达的生成。我们对生成的合成数据进行了全面评估，通过聚类和分布指标评估语义多样性，通过基于LLM的质量评分评估类人性，通过与真实世界情感语料库的比较评估真实性，以及在下游情感分类任务中的实用效用。实验结果表明，PersonaGen在生成多样化、连贯和具有区分性的情感表达方面显著优于基线方法，展示了其作为增强或替代真实世界情感数据集的稳健替代方案的潜力。
英文摘要：
In the field of emotion recognition, the development of high-performance models remains a challenge due to the scarcity of high-quality, diverse emotional datasets. Emotional expressions are inherently subjective, shaped by individual personality traits, socio-cultural backgrounds, and contextual factors, making large-scale, generalizable data collection both ethically and practically difficult. To address this issue, we introduce PersonaGen, a novel framework for generating emotionally rich text using a Large Language Model (LLM) through multi-stage persona-based conditioning. PersonaGen constructs layered virtual personas by combining demographic attributes, socio-cultural backgrounds, and detailed situational contexts, which are then used to guide emotion expression generation. We conduct comprehensive evaluations of the generated synthetic data, assessing semantic diversity through clustering and distributional metrics, human-likeness via LLM-based quality scoring, realism through comparison with real-world emotion corpora, and practical utility in downstream emotion classification tasks. Experimental results show that PersonaGen significantly outperforms baseline methods in generating diverse, coherent, and discriminative emotion expressions, demonstrating its potential as a robust alternative for augmenting or replacing real-world emotional datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
