From Emotion Classification to Emotional Reasoning: Enhancing Emotional Intelligence in Large Language Models
This work addresses the problem of enhancing emotional intelligence in AI models for applications like therapy or human-computer interaction, but it is incremental as it applies existing fine-tuning methods to new synthetic data.
The paper tackled improving emotional reasoning in smaller open large language models by fine-tuning them on synthetic emotional chain-of-thought data, resulting in Mistral 7B achieving EU improvements from 10.5 to 20.5 and EA improvements from 40.5 to 60.0 on EmoBench-style evaluations.
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.