Advancing Emotion Recognition through LLaMA3 and LoRA Fine-Tuning

Abstract

Emotion recognition from text is a key task in natural language processing (NLP), with applications ranging from interactive chat systems and mental health detection to consumer feedback analysis. While various machine learning and deep learning techniques have been explored, the success of Large Language Models (LLMs) presents new opportunities revolutionizing how we interact with text. In this study, we harness LLaMA-3-8B for this task by applying LoRA (Low-Rank Adaptation), a highly effective fine-tuning technique that reduces computational and memory costs while maintaining high performance. Experiments on the ISEAR, Emotion for NLP and SemEval 2019 datasets demonstrate significant improvements in accuracy and efficiency over traditional approaches. This work highlights the potential of Llama-3-8B, combined with LoRA, to excel in complex language understanding tasks, particularly in emotion recognition.