A Novel Approach to Emotion Classification with Llama3-8B: Integrating LoRA for Efficient Training

Abstract

Emotion classification is a crucial task in Natural Language Processing (NLP) that involves detecting emotional states expressed in text. This paper explores the integration of two advanced methodologies—Llama3-8B, a large language model, and LoRA (Low-Rank Adaptation)—to enhance the efficiency of emotion classification. By combining the general language capabilities of Llama3-8B with the adaptive fine-tuning power of LoRA, we propose a novel approach to emotion recognition in text that minimizes computational demands without compromising accuracy. Our experiments demonstrate the effectiveness of LoRA in improving both training speed and performance, making it feasible to scale emotion classification tasks across diverse datasets. We also analyze the impact of this approach in real-world applications, such as social media sentiment analysis and customer service automation, highlighting its potential for deployment in large-scale systems. The findings provide new insights into efficient training techniques, paving the way for future research in emotion classification using powerful language models.