Methods and Applications of Fine-Tuning Llama-2 and Llama-Based Models: A Systematic Literature Analysis

This Systematic Literature Review (SLR) scrutinizes 20 papers focusing on methods and applications for fine-tuning the open-source Llama language model.The findings underscore the efficacy of instruction-based tuning, demonstrating high performance across diverse domains, including medicine and psychology.The fine-tuned version Llama-2-Chat model, aligned with human preferences, emerges as a preferred base for subsequent tuning efforts.Despite its promise, scalability hurdles persist due to demanding computational requirements.A critical gap in the existing literature lies in the absence of a balanced evaluation of trade-offs among various fine-tuning approaches.Moreover, ethical considerations, particularly addressing bias and associated risks, demand greater attention in the deployment of tuned models.In conclusion, while instruction tuning holds significant promise for specializing Llama variants, overcoming limitations related to resource constraints, safety, and transparency is imperative for responsible real-world impact.This refined understanding emphasizes the need for a comprehensive assessment of fine-tuning methods and a conscientious approach to ethical considerations in the evolving landscape of large language models.