RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| author | Balaguer, Angels Benara, Vinamra Cunha, Renato Luiz de Freitas Filho, Roberto de M. Estevão Hendry, Todd Holstein, Daniel Marsman, Jennifer Mecklenburg, Nick Malvar, Sara Nunes, Leonardo O. Padilha, Rafael Sharp, Morris Silva, Bruno Sharma, Swati Aski, Vijay Chandra, Ranveer |
| author_facet | Balaguer, Angels Benara, Vinamra Cunha, Renato Luiz de Freitas Filho, Roberto de M. Estevão Hendry, Todd Holstein, Daniel Marsman, Jennifer Mecklenburg, Nick Malvar, Sara Nunes, Leonardo O. Padilha, Rafael Sharp, Morris Silva, Bruno Sharma, Swati Aski, Vijay Chandra, Ranveer |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_08406 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture Balaguer, Angels Benara, Vinamra Cunha, Renato Luiz de Freitas Filho, Roberto de M. Estevão Hendry, Todd Holstein, Daniel Marsman, Jennifer Mecklenburg, Nick Malvar, Sara Nunes, Leonardo O. Padilha, Rafael Sharp, Morris Silva, Bruno Sharma, Swati Aski, Vijay Chandra, Ranveer Computation and Language Machine Learning 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. |
| title | RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2401.08406 |