Optimizing AI Models with Fine-Tuning and RAG — Which Approach Wins?

Author(s): Konstantin Babenko

Originally published on Towards AI.

In today’s constantly evolving world of artificial intelligence, the opportunity to adapt the models for the given tasks is an enormous step forward. Two strategies, that is, fine-tuning and Retrieval-Augmented Generation (RAG), can be considered as ways of using the full potential of the AI models. In the article, we will explore these approaches, highlight their strengths and weaknesses, and establish when to apply them in your AI initiatives.

Transfer Learning and Other Techniques to Fine-Tune AI Models for Specific Performance

Transfer learning is a common procedure in AI where a pre-existing machine learning model is trained on a certain dataset for a particular task and then fine-tuned for a different task. This brings the model closer to the application to perform better on the specific application of interest.

Fine-tuning uses some of the knowledge that was gathered by the model during the initial training and then refines such knowledge to suit a specific dataset. For instance, a language model trained with a large text corpus can be fine-tuned to generate legal briefings, medical reports, or any other sub-field of writing.

The advantage of fine-tuning is that the models are endowed with the knowledge of a specific field instead of building a new model. This means that fine-tuning is very feasible and effective.

Workflow of Fine-Tuning

The process of fine-tuning generally involves three main stages, which are described below.

1. Data Preparation

Acquiring a dataset of a similar distribution as the target task, and pre-processing as well as annotating it. This data must include quality examples of what one would expect in terms of inputs and the corresponding outputs.

2. Model Retraining

Training the model for multiple iterations where the model has to update its parameters with respect to new data. This makes it possible for it to capture finer elements such as terms, style, and patterns inherent to the domain.

3. Evaluation

Validating the performance of the model on a new, unseen validation data set to check whether the accuracy criteria have been met before going live.

Advantages and Limitations of Fine-Tuning

There is no doubt that fine-tuning has both advantages and disadvantages. On the positive side, fine-tuning can quickly teach a model something related to a specific domain, including the choice of words, writing style, and handling of special situations. For instance, when a general model is fine-tuned on medical texts, the model becomes a medical expert and learns medical vocabulary. This domain expertise results in more appropriate, precise, and fluent answers. Fine-tuning also needs much less data and computational power than training a model from scratch.

But there are also some disadvantages that one has to consider. Fine-tuned models require higher quality and larger fine-tuning data sets to yield better performances. Smaller datasets or datasets that are skewed heavily in one particular direction can lead to over-training which makes the model less versatile. Also, focusing on one area, the model ceases to be as knowledgeable of the general information that general models are built upon in order to serve such a wide variety of purposes. A medical model would be highly proficient in the use of medical language, but not in other areas of communication.

In practice, the advantages often overrule the limitations when a specialized model is indeed required.

Improving AI’s Fact-Based Responses with RAG

Retrieval-Augmented Generation (RAG) is a relatively new approach that allows AI to provide more accurate and fact-based natural language responses.

In essence, in RAG, external knowledge is integrated into a language generation model. Such data can be from databases, documents, web content, or other corpora. In this way, the RAG system can generate responses that incorporate factual and specific contextual information.

For example, an e-learning platform may utilize the RAG system to enhance the learning of their students. Being able to access sources such as books, papers, and multimedia, RAG models can provide individual explanations and examples based on the concepts that a given student may need clarification on.

The Architecture of RAG Models

RAG consists of two main components working together:

Retrieval Component

This serves the role of a ‘librarian’ where it borrows or searches for knowledge from different sources to retrieve information relevant to a certain query or context. In other words, it seeks recommendations for what information would be beneficial to acquire.

Generation Component

This involves incorporating the context that has been retrieved into a natural language response. In this case, language modeling is applied to analyze the external information and reform it in a piece of text that is meaningful in the given context.

Key Advantages of RAG

RAG architectures offer several benefits:

– Contextual understanding. RAG efficiently performs comprehension-focused tasks because it pulls data from various sources.

– Reduced bias and hallucination. It reduces the hallucinated text or biases from the training data while grounding the responses in the retrieved evidence.

– Scalability via transfer learning. The model can transfer-learn from very large datasets external to the application domain and can keep on learning.

– Interpretability. This makes the RAG prediction process more transparent because the various components can be examined individually.

Limitations of RAG

Despite the numerous advantages of applying RAG, some limitations remain:

– Retrieval quality. The overall efficiency depends on the capacity of the system to search for the right and appropriate information.

– Complex implementation. When it comes to applying RAG, it could become complicated simply because of combining the components as well as dealing with the external data.

– Data reliance. The computational results strongly depend on the presence of a current and adequate external source of information that should be properly pre-filtered for the analysis.

In conclusion, RAG improves the language generation component because it allows models to incorporate knowledge from other sources. Even though this approach is not without drawbacks, it may be the key to building wiser and more factually accurate NL systems.

Weighing the Benefits of Fine-Tuning vs. RAG

This section compares RAG with fine-tuning approaches and gives you an insight into the considerations to take into account when choosing between the two techniques. Before we start, let’s quickly recap what each technique stands for.

RAG systems receive inputs and actively acquire and incorporate external knowledge for their responses. Like a virtual research assistant, RAG models utilize outside information to strengthen output by providing appropriate facts and background information. This makes them well-equipped to attend to a wide variety of prompts without sacrificing abilities.

Fine-tuning means retraining a large language model on the new data to perform specific tasks or to suit a specific domain.

But how do you decide which of the two is preferable for your requirements or purpose? Here are some key factors to consider:

Task Complexity

By offering ideas from its knowledge base and the context outside of the LLM’s original training mission, RAG is most beneficial when your task is similar to what the LLM was trained to do. In more specific cases, fine-tuning may transmit the necessary abilities more effectively.

Data Availability

Fine-tuning involves the use of specific datasets for a particular task which can be expensive or time-consuming to come across or build. The amount of performance of RAG mainly depends on the quality of the retrieval system and the knowledge sources.

Domain Expertise

For instance, if there is a need to create domain-specific terms or to write in a certain way or for a specific audience, fine-tuning can address the need for an LLM more easily.

Factual Accuracy

RAG systems have the capability of using real-time factual data that eliminates chances of making mistakes and improves response accuracy. Moreover, fine-tuning alone exposes the model to a potential loop of perpetuating non-recent or wrong knowledge.

Interpretability

RAG offers more transparency into the LLM’s actions since the stages of retrieval and text production are distinct. Differential tuning leads to a more ‘black box’ outcome.

Computer Requirements

While fine-tuning requires fewer computations compared to full LLM training, the retrieval of RAG can be costly, particularly when dealing with huge data sets.

Recommendations by Model Size

The optimal strategy also depends significantly on your model’s size:

Large Models: RAG Preferred

When comparing capabilities for models with trillions of parameters like GPT-4, RAG is superior in maintaining ability while improving performance.

Mid-Sized Models: Balance Both

Fine-tuning enhances memorization-intense tasks for models with billions of parameters while RAG focuses on domain-specific generation.

Small Models: Fine-Tuning Prioritized

Custom small models also benefit from efficient fine-tuning in terms of imparting specific knowledge with relatively high risks.

Merging Fine-Tuning with RAG for Dynamic AI Models

When fine-tuning is used to adapt models and combined with the ability of RAG to dynamically retrieve information, it makes for highly efficient adaptive AI. Altogether, these two methods make it possible to create AI systems that can update themselves with new information from the web and then filter the information for the user and context. The fine-tuning is constantly modifying the model over time as the system is used while the retrieval provides the most recent data all the time.

However, this blend does mean that more computing power is needed as both approaches are computationally expensive. Fine-tuning requires additional data for models’ refinement, which requires time and computing resources. Retrieval systems have to organize and provide access to huge collections of documents to search. Yet, the payoff is that the system can remain as current as possible.

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Published via Towards AI