DialogueLLM: Context and Emotion Knowledge-Tuned Large Language Models for Emotion Recognition in Conversations

• URL: http://arxiv.org/abs/2310.11374v4
• Date: Wed, 17 Jan 2024 02:44:56 GMT
• Title: DialogueLLM: Context and Emotion Knowledge-Tuned Large Language Models for Emotion Recognition in Conversations
• Authors: Yazhou Zhang, Mengyao Wang, Youxi Wu, Prayag Tiwari, Qiuchi Li, Benyou Wang, Jing Qin
• Abstract summary: Large language models (LLMs) have shown extraordinary efficacy across numerous downstream natural language processing (NLP) tasks. We propose DialogueLLM, a context and emotion knowledge tuned LLM that is obtained by fine-tuning LLaMA models. We offer a comprehensive evaluation of our proposed model on three benchmarking emotion recognition in conversations datasets.
• Score: 28.15933355881604
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) and their variants have shown extraordinary efficacy across numerous downstream natural language processing (NLP) tasks, which has presented a new vision for the development of NLP. Despite their remarkable performance in natural language generating (NLG), 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 LLMs is that they are typical trained without leveraging multi-modal information. To overcome these limitations, we propose DialogueLLM, a context and emotion knowledge tuned LLM that is obtained by fine-tuning LLaMA models with 13,638 multi-modal (i.e., texts and videos) emotional dialogues. The visual information is considered as the supplementary knowledge to construct high-quality instructions. We offer a comprehensive evaluation of our proposed model on three benchmarking emotion recognition in conversations (ERC) datasets and compare the results against the SOTA baselines and other SOTA LLMs. Additionally, DialogueLLM-7B can be easily trained using LoRA on a 40GB A100 GPU in 5 hours, facilitating reproducibility for other researchers.

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