Understanding Fine-Tuning: How AI Models Get Tailored for Precision

Source: https://kumo.ai/learning-center/understanding-what-is-fine-tuning-in-ai-a-beginners-guide/

Most companies integrating AI into their business rely on general purpose models like GPT-4 and Claude Sonnet. However, this is like using a Swiss Army knife instead of a surgical scalpel — it can get the job done, but not as precisely or effectively as a specialized tool. This is where fine-tuning excels.

What is fine-tuning

Fine-tuning is a process of taking an existing AI model, and training it further for a specific task or purpose, using new data not included in the original training set. At the same time, you are not building a model from scratch (which can be extremely costly). Think of it as training a new employee (which already has some set of skills) to follow your company’s unique protocols and preferred ways of working.

For example, hospitals and clinics can fine-tune models on patient symptom data, common diagnostic protocols, and standard medical jargon to generate tailored diagnostic suggestions. Another example — a real estate agency can fine-tune a model to recognize real estate-specific terms and details, such as property types, square footage, and local market insights to answer client questions more accurately, or even predict what types of listings might appeal to specific buyer segments.

To be clear, fine-tuning can be done not just on LLMs (large language models), but pretty much on any AI models (e.g. you can fine-tune an image generation model to make new images in Van Gogh style).

Why you should fine-tune your models

There are in fact multiple benefits of using a fine-tuned AI model over a generic one:

• Better accuracy — a fine-tuned model generates outputs tailored specifically to your use case, delivering more relevant results.
• Faster and cheaper — a smaller fine-tuned model can perform as well (or even better than) as a larger general-purpose model. Plus, smaller models are faster and cheaper to run. Besides, less clarification is needed in the prompt, hence less tokens in the input, which decreases time and costs even further.
• Domain-specific knowledge — generic models may lack detailed knowledge in certain fields. Fine-tuning with domain-specific data makes sure the model understands relevant terminology and concepts, hence reducing the risk of hallucinations (which occur when the model encounters unfamiliar topics).
• Better personalization — fine-tuning allows the model to adopt a specific style, tone, and format that align with your brand or requirements.
• Leverage your unique data — today data is a new gold, and utilizing your unique data in a model can be very valuable, which is what generic models cannot offer.

Types of fine-tuning

There are different types of fine-tuning:

• Task-Specific — fine-tuning a model for a specific task (e.g. question answering, summarization, entity recognition, sentiment analysis, etc.).
• Domain-specific — adapting a model to perform better within a particular field or industry, like real estate, legal, or medical.
• Language-specific — fine-tuning a model to improve its performance with a particular language or dialect.
• Style or tone — adjusting a model to reflect a specific voice or style, such as formal vs. informal or technical vs. conversational.

How to fine-tune a model (high-level)

1. The first step is to select a pre-trained model as a base. Some of the popular large language models for fine-tuning are BERT, RoBERTa, GPT-3, Llama3 and Mistral models.
2. Then you need to prepare a dataset that is relevant to your specific task or domain. Usually it is just a list of input-output pairs. Size of the fine-tuning dataset depends on the complexity of the task. For example, for a simple task like a sentiment analysis you need at least 1,000 data points for the fine-tuning, while some more complex use-cases such as customer service often require over 100,000 data points for the fine-tuning.
3. Define training parameters, such as batch size, learning rate, number of epochs, weight decay, evaluation metrics, and others.
4. Additionally, you can tweak some parts of the architecture, e.g. add or modify some of the model layers.
5. Finally, run the training!

Conclusion

As businesses begin to integrate AI into their workflows, many start by simply adopting popular models like GPT-4 and Claude Sonnet. However, as they become more experienced with AI, they’ll recognize that using general-purpose models often falls short in terms of accuracy, efficiency, cost, and effective use of unique data. Fine-tuning can efficiently solve those issues by optimizing models to perform specific tasks with precision, speed, and relevance. By tailoring AI systems to their unique needs, businesses can get the most out of their AI models.