Optimized Fine-Tuning of Large Language Model for Better Topic Categorization with Limited Data

Abstract The novel approach of fine-tuning the Llama model for topic categorization with limited data presents significant advancements, offering enhanced performance and reduced hallucinations compared to traditional models. Employing transfer learning and data augmentation techniques, the research demonstrates the model's ability to adapt to diverse thematic domains, achieving high accuracy in topic categorization. Comprehensive comparative analysis with baseline models demonsrates the superiority of the fine-tuned Llama model, highlighting the effectiveness of advanced training methodologies and hyperparameter optimization strategies. Additionally, the analysis of hallucinations illustrates the robustness of implemented mitigation techniques, thereby enhancing the reliability of generated text. Computational efficiency assessment confirms the practical feasibility of deploying the fine-tuned model, balancing resource utilization with performance gains. The study substantiates the potential of large language models to be fine-tuned effectively for specialized tasks, contributing valuable insights to the field of natural language processing.