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Introduction

  • RAG (Retrieval-Augmented Generation) augments the prompt with external data, while fine-tuning incorporates additional knowledge directly into the model.

  • Both RAG and fine-tuning are effective techniques for improving the performance of Large Language Models (LLMs).

  • RAG is highly effective in instances where data is contextually relevant, leading to more succinct responses.

  • Fine-tuning is useful in teaching the model new skills specific to a domain, providing more precise and succinct responses.

  • The study shows an accuracy increase of over 6 percentage points when fine-tuning the model, cumulative with RAG, which increases accuracy by 5 percentage points further.

  • In agriculture, the fine-tuned model leverages information from across geographies to answer specific questions, increasing answer similarity from 47% to 72%.

  • The paper proposes a comprehensive pipeline for both RAG and fine-tuning, including stages like extracting information from PDFs, generating questions and answers, and evaluating results using GPT-4.

Introduction [1]

  • Overview: The paper discusses two common methods for incorporating proprietary and domain-specific data into LLMs: Retrieval-Augmented Generation (RAG) and fine-tuning.

  • Purpose: The study aims to propose a pipeline for both RAG and fine-tuning and present their tradeoffs for popular LLMs.

  • Models: The study evaluates multiple popular LLMs, including Llama2-13B, GPT-3.5, and GPT-4.

  • Industry Focus: The paper includes a detailed case study in agriculture, a field that has not seen much penetration of AI.

  • Potential Impact: The study aims to pave the way for further applications of LLMs in other industrial domains.

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Methodology [1]

  • Pipeline: The proposed pipeline includes stages like extracting information from PDFs, generating questions and answers, and evaluating results using GPT-4.

  • Data Acquisition: The initial focus is on gathering a diverse and curated dataset pertinent to the industry domain.

  • Information Extraction: Robust text extraction tools and machine learning algorithms are used to recover textual, tabular, and visual information from PDFs.

  • Question Generation: The methodology employs a framework to control the structural composition of both inputs and outputs, enhancing the overall efficacy of response generation.

  • Answer Generation: Retrieval-Augmented Generation (RAG) combines retrieval and generation mechanisms to create high-quality answers.

  • Fine-Tuning: The models are fine-tuned with the generated Q&A pairs, employing methods like Low Rank Adaptation (LoRA).

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Results [2]

  • Accuracy Improvement: The study shows an accuracy increase of over 6 percentage points when fine-tuning the model, cumulative with RAG, which increases accuracy by 5 percentage points further.

  • Geographic Knowledge: The fine-tuned model leverages information from across geographies to answer specific questions, increasing answer similarity from 47% to 72%.

  • Evaluation Metrics: The study proposes metrics to assess the performance of different stages of the RAG and fine-tuning pipeline.

  • Model Performance: GPT-4 consistently outperformed other models, but the cost associated with its fine-tuning and inference needs to be considered.

  • Qualitative Benefits: The results show the effectiveness of the dataset generation pipeline in capturing geographic-specific knowledge.

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Tradeoffs [1]

  • RAG Benefits: Highly effective in instances where data is contextually relevant, leading to more succinct responses.

  • Fine-Tuning Benefits: Useful in teaching the model new skills specific to a domain, providing more precise and succinct responses.

  • Cost Considerations: Fine-tuning requires extensive work and has a high initial cost.

  • Contextual Relevance: RAG is particularly effective in interpreting farm data and providing contextually relevant answers.

  • Skill Acquisition: Fine-tuning helps the model acquire new domain-specific skills, enhancing its overall performance.

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Case Study [2]

  • Industry Focus: The case study focuses on agriculture, a field that has not seen much penetration of AI.

  • Dataset: The study uses an agricultural dataset to evaluate the effectiveness of the proposed pipeline.

  • Geographic Insights: The study aims to provide location-specific insights to farmers, capturing geographic-specific knowledge.

  • Disruptive Application: The study explores the potential of providing location-specific insights to farmers, a potentially disruptive application in agriculture.

  • Evaluation: The study evaluates the performance of various models in generating question-answer pairs within the context of agricultural data.

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Related Videos

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<div class="-md-ext-youtube-widget"> { "title": "RAG vs Fine-tuning", "link": "https://www.youtube.com/watch?v=EbEPHOABgSY", "channel": { "name": ""}, "published_date": "Feb 7, 2024", "length": "" }</div>

<div class="-md-ext-youtube-widget"> { "title": "Fine-tuning vs RAG", "link": "https://www.youtube.com/watch?v=eDW8XIGP6Sw", "channel": { "name": ""}, "published_date": "Feb 10, 2024", "length": "" }</div>