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.

翻译：大型语言模型及其变体在众多下游自然语言处理任务中展现出非凡效能，为自然语言处理领域的发展提供了新视野。尽管大语言模型在自然语言生成方面表现卓越，但其在情感理解领域缺乏明确聚焦，导致直接将其应用于情感识别可能产生次优且精度不足的结果。此外，大语言模型通常未利用多模态信息进行训练。为突破这些局限，我们提出DialogueLLM——一种经由上下文与情感知识调优的LLM模型，通过使用13,638组多模态（文本与视频）情感对话对LLaMA模型进行微调获得。视觉信息被作为辅助知识用于构建高质量指令。我们在三个基准对话情感识别数据集上对模型进行了全面评估，并将结果与最先进基线及其他顶尖大语言模型进行对比。值得注意的是，DialogueLLM-7B可通过LoRA技术在40GB A100 GPU上5小时内轻松完成训练，便于其他研究者复现。