arxiv preprint - RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

In this episode, we discuss RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture by Angels Balaguer, Vinamra Benara, Renato Luiz de Freitas Cunha, Roberto de M. Estevão Filho, Todd Hendry, Daniel Holstein, Jennifer Marsman, Nick Mecklenburg, Sara Malvar, Leonardo O. Nunes, Rafael Padilha, Morris Sharp, Bruno Silva, Swati Sharma, Vijay Aski, Ranveer Chandra. The paper explores two methods of integrating specialized data into Large Language Models (LLMs): Retrieval-Augmented Generation (RAG), which adds external data to the input, and Fine-Tuning, which embeds the data into the model itself. A multi-stage pipeline for these methods is tested on an agricultural dataset to evaluate their effectiveness in providing geographically tailored insights to farmers. Results indicate substantial improvements in accuracy (over 6 percentage points with Fine-Tuning and an additional 5 with RAG), with fine-tuned models effectively using cross-regional information, showcasing the potential for LLMs to be customized for industry-specific applications.