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

Jun 27, 2024

Exploring the effectiveness of RAG vs Fine-tuning methods in Large Language Models for agriculture applications. Discussing the generation of questions and answers, nutrient management in citrus fruits, evaluating question quality, and performance of base vs fine-tuned models with RAG integration. Revealing insights on location-specific agricultural knowledge and the benefits of utilizing both approaches.

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Intro

00:00 • 19min

Preprocessing of Files and Benchmarking for Q&A Generation in Agriculture

18:37 • 7min

Nutrient Management in Citrus Fruits: Borax Application and Best Practices

25:37 • 2min

Evaluating Question Quality in Large Language Models

27:38 • 26min

Performance Evaluation of Base and Fine-Tuned Models with RAG Integration in Agriculture

53:41 • 13min

There are two common ways in which developers are incorporating proprietary and domain-specific data when building applications of Large Language Models (LLMs): Retrieval-Augmented Generation (RAG) and Fine-Tuning. RAG augments the prompt with the external data, while fine-Tuning incorporates the additional knowledge into the model itself. However, the pros and cons of both approaches are not well understood. In this paper, we propose a pipeline for fine-tuning and RAG, and present the tradeoffs of both for multiple popular LLMs, including Llama2-13B, GPT-3.5, and GPT-4. Our pipeline consists of multiple stages, including extracting information from PDFs, generating questions and answers, using them for fine-tuning, and leveraging GPT-4 for evaluating the results. We propose metrics to assess the performance of different stages of the RAG and fine-Tuning pipeline. We conduct an in-depth study on an agricultural dataset. Agriculture as an industry has not seen much penetration of AI, and we study a potentially disruptive application - what if we could provide location-specific insights to a farmer? Our results show the effectiveness of our dataset generation pipeline in capturing geographic-specific knowledge, and the quantitative and qualitative benefits of RAG and fine-tuning. We see an accuracy increase of over 6 p.p. when fine-tuning the model and this is cumulative with RAG, which increases accuracy by 5 p.p. further. In one particular experiment, we also demonstrate that the fine-tuned model leverages information from across geographies to answer specific questions, increasing answer similarity from 47% to 72%. Overall, the results point to how systems built using LLMs can be adapted to respond and incorporate knowledge across a dimension that is critical for a specific industry, paving the way for further applications of LLMs in other industrial domains.

2024: M. A. D. L. Balaguer, Vinamra Benara, Renato Luiz de Freitas Cunha, Roberto de M. Estevao Filho, Todd Hendry, Daniel Holstein, Jennifer Marsman, Nick Mecklenburg, S. Malvar, Leonardo Nunes, Rafael Padilha, Morris Sharp, B. Silva, Swati Sharma, Vijay Aski, Ranveer Chandra

https://arxiv.org/pdf/2401.08406