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

• URL: http://arxiv.org/abs/2401.08406v3
• Date: Tue, 30 Jan 2024 13:55:34 GMT
• Title: RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
• Authors: Angels Balaguer, Vinamra Benara, Renato Luiz de Freitas Cunha, Roberto de M. Estev\~ao 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
• Abstract summary: We propose a pipeline for fine-tuning and RAG, and present the tradeoffs of both for popular Large Language Models. Our results show the effectiveness of our dataset generation pipeline in capturing geographic-specific knowledge.
• Score: 2.4184993026516213
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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