用合成情绪推理数据训练小模型，提升其情感理解能力。
From Emotion Classification to Emotional Reasoning: Enhancing Emotional Intelligence in Large Language Models
- 构建多智能体系统生成治疗式对话并转为带解释的情绪选择题。
- 微调后Mistral 7B在情感理解与情感意识上分别提升至20.5和60.0。
- 无需修改模型结构，即可通过数据增强实现情感推理能力提升。
本文研究了合成情绪链式思维数据是否能提升小型开源大语言模型的情感推理能力。我们设计了一个多智能体生成流程，生成类似心理治疗的对话，并将其转化为带有解释的结构化情绪多选题（MCQs）。我们提出，在该数据集上微调多种7B规模的模型，应在EmoBench类评估中显著提升情感理解与情感意识表现，表明情感推理可通过数据诱导而无需架构改动。实验结果表明，微调后的Mistral 7B在情感理解（EU）上从10.5提升至20.5，在情感意识（EA）上从40.5提升至60.0，验证了合成情绪推理数据在提升模型处理细微情感任务能力方面的有效性。
原文摘要 · Abstract (English)
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.
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