This work investigates whether synthetic emotional chain-of-thought data can improve the emotional reasoning abilities of smaller open large language models (LLMs). We design a multi-agent generation pipeline that produces therapy-style conversations and converts them into structured emotion multiple-choice questions (MCQs) with explanations. We propose that fine-tuning a variety of 7B models on this dataset should yield substantial gains in emotional understanding and emotional awareness on EmoBench-style evaluations, suggesting that emotional reasoning can be induced without architectural changes. Our results demonstrate that fine-tuned Mistral 7B achieves EU improvements from 10.5 to 20.5 and EA improvements from 40.5 to 60.0, validating the effectiveness of synthetic emotional reasoning data for enhancing model capabilities in nuanced emotional tasks.

翻译：本研究探讨合成情感思维链数据能否提升小型开源大型语言模型（LLMs）的情感推理能力。我们设计了一个多智能体生成流程，用于产生治疗式对话并将其转化为带解释的结构化情感多项选择题（MCQs）。我们提出，基于该数据集对多种7B模型进行微调，应能在EmoBench式评估中显著提升情感理解（EU）与情感意识（EA）指标，这表明无需改变模型架构即可实现情感推理能力的诱导。实验结果表明，微调后的Mistral 7B模型将EU指标从10.5提升至20.5，EA指标从40.5提升至60.0，验证了合成情感推理数据在增强模型执行精细情感任务能力方面的有效性。