Generative AI has emerged as a powerful productivity tool across various industries, transforming the way businesses interact with data, customers, internal processes, and more. The technology's seemingly endless list of abilities, from writing content, to creating highly customized images, help predict outcomes, and automate tasks has quickly led to a growing demand for highly specialized models to take on or support complex knowledge-driven tasks. These specialized AI models are designed to meet specific needs, providing more personalized and efficient solutions, and for businesses, a much-desired edge against the competition.
Enter fine-tuning, or the ability to tweak a Large Language Model (LLM) to amplify or deepen its knowledge base for a specific area, like adding historic deliverables from your organization so analysts can tap into a very niche knowledge base with ease.
In a previous post, I discussed the challenges of fine-tuning and how embeddings were the next best alternative. I won’t go into details here, but long story short:
1. Fine-tuning is impractical:
a. It’s expensive
b. Need access to the raw model
c. Need for large volumes of high quality data
2. Embeddings can address most needs covered by fine-tuning
Despite these hurdles, the pursuit of fine-tuned generative AI continues to be a priority for many, as the potential benefits far outweigh the challenges.
And then OpenAI releases it’s new ChatGPT feature: fine-tuning.
Fine-tuning is the process of taking an already trained AI model and adapting it to a specific task or domain. It involves adjusting the model's parameters to align with the unique requirements of the targeted application.
Unlike general training, where a model is trained from scratch using a large dataset, fine-tuning leverages the existing knowledge of a pre-trained model. It focuses on refining the model for a particular task, using a smaller, domain-specific dataset. This approach often leads to faster training and better performance for specialized tasks.
The idea of having highly specialized LLMs at hand is what makes this concept so powerful: the laser-focus on a specific area. Think of it as the equivalent output of a niche consultant with all-knowing powers over your historical data, beyond the specialty they provide. Some use cases:
1. Domain assistant: imagine your very own expert on, say, ops research. Now your “knowledgeable” analyst is supercharged and can access a vast body of knowledge with ease, all the while combining corporate knowledge, previous projects, CRM, ERP, etc.
2. Chatbot with a personality: what if your customer-facing website’s chatbot not only had deep understanding of your solutions but could also connect with your audience beyond language and into expression. A bot that speaks like an actor, or a movie character, something very specific to your target audience or even a specific individual.
3. Your own commission artist: if you’ve used any image generation AI or used it to write anything, you will have noticed how generic the output can become, unless you put some real effort on your prompt engineering. A fine-tuned model to cater to your specific needs is more likely to create an output much closer to what you need with much less effort, leading to much desired consistency.
4. Hyper-personalized recommendations at scale: when you can process large volumes of data and have a highly specialized LLM to process and connect, it is easy to think of how companies can use fine-tuning to create hyper-personalized experiences for their customers.
At a high level, fine-tuning a Large Language Model – or most models, for that matter – consist of 6 stages.
1. Selection of Pre-Trained Model
2. Data Preparation
3. Model Adaptation
4. Training with Domain-Specific Data
5. Evaluation and Optimization
6. Deployment
It can seemingly feel straight-forward, but the reality is much more nuanced, with every iteration potentially becoming quite costly.
Choosing an appropriate pre-trained model is the first step. The selected model should have architecture and features that align with the specific task. This ensures that the model has a strong foundation to build upon.
The data used for fine-tuning must be relevant to the task at hand. It involves collecting, cleaning, and preprocessing domain-specific data to ensure that the model can learn the nuances of the targeted application.
This step involves adjusting the layers and parameters of the pre-trained model. It may include freezing certain layers to retain their learned features and modifying others to better suit the new task.
The adapted model is then trained using the prepared domain-specific data. This process refines the model's understanding of the task, allowing it to make more accurate predictions or generate more relevant content.
After training, the model is evaluated using various metrics to assess its performance. Fine-tuning may require several iterations of adjustment and retraining to achieve optimal results.
Once fine-tuned, the model is integrated into the desired application or system. Proper deployment ensures that the model functions effectively in its intended environment. This is the world of AIOps - subscribe to learn more on this topic.
Fine-tuning offers a pathway to harness the power of generative AI in a more targeted and efficient manner. By building on the strengths of pre-trained models and adapting them to specific needs, fine-tuning enables organizations to create highly specialized AI solutions without the need for extensive resources or expertise.
As we’ve seen before, the addition of embeddings can allow organizations to leverage their own data, with much less complexity and direct costs.
Working with LLMs can still be intimidating and there is much skepticism fueled by so much noise and a few notorious misuses of this technology. Businesses should approach Generative AI technology with caution, but it should be so with every new disruptive technology. Having a strong AI or Generative AI Strategy can greatly reduce risks and systematically bring in incremental innovations and improvements to virtually every aspect of the business.
Click here to learn more about how Phi Research can help your business harness this technology.
Fine-tuning pre-trained Large Language Models (LLM) can help businesses gain strong differentiation by spinning out highly specialized solutions.
Fine-tuning can be costly and complex, but solutions like the latest OpenAI’s commercial solution to fine-tune its flag product ChatGPT, is making this option accessible to organizations and individuals at reasonable costs. And I am certain that competitors and the open source echosystem will respond quickly with similar alternatives.
The process of fine-tuning LLMs continues to be relatively complex, so organizations that begin experimenting with this technology early on can gain competitive advantages, when done properly.
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