RAG, RAG Fusion, and Fine-Tuning: A Comparative Analysis

Frank Morales Aguilera, BEng, MEng, SMIEEE

Boeing Associate Technical Fellow /Engineer /Scientist /Inventor /Cloud Solution Architect /Software Developer /@ Boeing Global Services

Introduction

Large Language Models (LLMs) like GPT-4 have transformed how we interact with AI, enabling everything from human-like conversation to creative content generation. However, harnessing their full potential for specific tasks often requires tailoring their capabilities. This is where techniques like RAG, RAG fusion, and fine-tuning come into play.

Understanding RAG

Retrieval-augmented generation (RAG) is a powerful approach that enhances an LLM’s knowledge base by incorporating external information sources during the generation process. Here’s how it works:

1. Retrieval: When a query is posed, the model retrieves relevant documents or passages from a knowledge base.
2. Augmentation: This retrieved information is used to augment the LLM’s context, providing it with the most up-to-date and domain-specific knowledge.
3. Generation: The LLM generates a response based on its internal knowledge and the augmented context.

RAG is handy when dealing with rapidly changing information, such as news or scientific discoveries, as it ensures the model’s responses are grounded in the latest data.

RAG Fusion: Taking Collaboration to the Next Level

RAG fusion builds upon the RAG framework by incorporating multiple LLMs into the retrieval and generation process. This collaborative approach offers several advantages:

• Enhanced Expertise: Each LLM can specialize in a particular domain or task, contributing its unique expertise to the final response.
• Increased Diversity: The diverse perspectives of multiple models can lead to more comprehensive and nuanced answers.
• Reduced Hallucinations: Collaboration between models can help mitigate the risk of factual errors or “hallucinations.”

Fine-Tuning: Tailoring the Model to Your Needs

Fine-tuning involves training an LLM on a smaller, task-specific dataset to adapt its parameters and behaviour to a specific domain or use case. This process allows the model to become more specialized and proficient at sentiment analysis, translation, or code generation tasks.

Fine-tuning requires a significant amount of labelled data and computational resources, but it can substantially improve performance and accuracy compared to using a general-purpose LLM.

Choosing the Right Approach

The optimal approach depends on your specific requirements and resources:

• RAG: Ideal for scenarios where up-to-date information is crucial and external knowledge sources are available.
• RAG Fusion: Best suited for complex tasks requiring the expertise of multiple models or when diversity of perspectives is desired.
• Fine-tuning is preferred when there is a large amount of labelled data, and you want to optimize the model for a specific task.

The Future of LLM Customization

As research in this field progresses, we can expect even more sophisticated techniques for customizing and optimizing LLMs. Combining RAG, RAG fusion, and fine-tuning in innovative ways could lead to even more powerful and versatile AI models tackling a more comprehensive range of tasks.

Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation (RAG) is a methodology that enhances the capabilities of large language models (LLMs) by integrating retrieval-based and generative components[1].

RAG fetches relevant information from large collections of texts (e.g., Wikipedia, a search engine index, or a proprietary dataset) and fuses this external knowledge into the generation process[2]. RAG’s advantage is its ability to dynamically update the pool of information it accesses, offering responses informed by the most up-to-date knowledge without retraining the model[2].

RAG-Fusion

RAG-Fusion is a variant of RAG that combines RAG and reciprocal rank fusion (RRF) by generating multiple queries, reranking them with reciprocal scores, and fusing the documents and scores[1, 3].

This method provides a richer context for the search[4].

By manually evaluating answers on accuracy, relevance, and comprehensiveness, it was found that RAG-Fusion could provide accurate and comprehensive answers because the generated queries contextualized the original query from various perspectives[1, 3]. However, some answers strayed off-topic when the generated queries’ relevance to the original query was insufficient[1, 3].

Fine-Tuning

Fine-tuning involves adjusting a pre-trained model’s parameters on a specific dataset to tailor the model to particular needs[2].

It is much easier than retraining the entire large language model, which requires massive computational effort[2]. Fine-tuning adapts a general-purpose model, such as one trained on diverse internet text, to perform well on more specialized content[2]. The technique is especially beneficial when the base model is already powerful but needs slight modifications to master a specific context, such as legal document analysis or sentiment detection in customer feedback[2].

Comparative Analysis

The core difference between RAG, RAG-Fusion, and fine-tuning lies in their approaches to leveraging external information. RAG and RAG-Fusion dynamically incorporate information from external sources at inference time, providing more comprehensive, context-aware responses[2, 5]. However, they don’t change the model’s inherent functioning [2].

On the other hand, fine-tuning integrates new information during the training phase, modifying the model to perform better on the target task [5]. This method refines the model’s ability to handle details and nuances of the targeted domain, making it more effective for specialized functions within a constrained context[2].

Case study

Several test cases were implemented and thoroughly tested in Google colab in the following references: Fine-tuning and RAG.

Conclusion

The choice between RAG, RAG-Fusion, and fine-tuning depends on the task’s specific requirements. While RAG and RAG-Fusion are excellent for generating up-to-date responses and eliminating hallucinations, fine-tuning is designed to augment an LLM’s domain-specific understanding, resulting in more accurate responses[6].