DialogueLLM: Context and emotion knowledge-tuned large language models for emotion recognition in conversations.

Abstract

Large language models (LLMs) and their variants have shown extraordinary efficacy across numerous downstream natural language processing tasks. Despite their remarkable performance in natural language generating, LLMs lack a distinct focus on the emotion understanding domain. As a result, using LLMs for emotion recognition may lead to suboptimal and inadequate precision. Another limitation of the current LLMs is that they are typically trained without leveraging multi-modal information. To overcome these limitations, we formally model emotion recognition as text generation tasks, and thus propose DialogueLLM, a context and emotion knowledge tuned LLM that is obtained by fine-tuning foundation large language models. In particular, it is a context-aware model, which can accurately capture the dynamics of emotions throughout the dialogue. We also prompt ERNIE Bot with expert-designed prompts to generate the textual descriptions of the videos. To support the training of emotional LLMs, we create a large scale dataset of over 24K utterances to serve as a knowledge corpus. Finally, we offer a comprehensive evaluation of DialogueLLM on three benchmarking datasets and significantly outperform 15 state-of-the-art baselines and 3 state-of-the-art LLMs. The emotion intelligence test shows that DialogueLLM achieves 109 score and surpasses 72 % humans. Additionally, DialogueLLM-7B can be easily reproduced using LoRA on a 40GB A100 GPU in 5 hours.