Boost GenAI accuracy: Fine-tune models with proprietary data

Generative artificial intelligence (GenAI) applications have found their way into nearly every industry, and into nearly every business unit of enterprise organizations. The underlying technology—large language models (LLMs)—shows incredibly powerful capabilities, and it just keeps getting better. However, real-world applications require top-notch accuracy and relevant responses. Base models are trained mostly on publicly available data. Even though it’s a massive amount of training data, the knowledge in these models is often insufficient. Enterprise GenAI applications need access to in-house proprietary data.

There are several ways to incorporate proprietary data into GenAI applications. These methods include large context windows, retrieval-augmented generation (RAG), and fine-tuning. Fine-tuning can be especially effective for improving the relevance and accuracy of a custom model, but only if enterprises use high-quality datasets with their proprietary data.

The value of proprietary datasets

LLMs may seem like magic; but in the end, they can only be as good as the data on which they were trained. Adding proprietary data to a model makes it aware of business-specific needs and tasks. When an LLM is fine-tuned on domain-specific data, it is much more effective at providing accurate and relevant answers to domain-specific queries.

By training an LLM on custom data, organizations gain a significant competitive advantage over rivals that utilize general-purpose LLMs only. Using an LLM tailored to an organization's knowledge base and operating procedure will greatly enhance the application's performance.

Benefits of using in-house data

If your enterprise uses in-house data to fine-tune your LLMs, you’ll experience significant benefits, which can include:

• Customization: In-house data allows you to customize your model to a specific industry or company. For example, John Snow Labs fine-tuned the BioBert base model with clinical notes to improve its capabilities in understanding patient electronic health records.
• Improved interaction: In-house data is very useful for training models for end-user support. Supplying in-house data provides important background and context to the model, yielding more effective and helpful GenAI interactions—for both applications used internally and with end users. For example, Qlerify collected business data from their industry and used it to fine-tune and significantly improve the output of its AI-based business process modeling.
• Data privacy and security: Using in-house data for fine-tuning gives you full control over the model. You can protect the privacy and security of your sensitive data as you use it to train your model.
• Performance: With a smaller model, custom language model training on in-house data may outperform a larger base model, which means you get a win-win of better performance at a lower cost. (A small model needs fewer compute resources.)

Developing custom language models

The process for how to train an LLM on your own data involves several steps. The intensity and complexity of the process makes it most suitable for large enterprises with a lot of proprietary data.

Data collection and preparation

The success of a custom LLM depends largely on the quality of its training data. It’s crucial to gather a diverse and comprehensive dataset that reflects the language, terminologies, and contexts relevant to the model’s intended use. 

Before the data can be used to fine-tune the model, it needs to be preprocessed. Preprocessing steps include data cleaning, tokenization, and normalization. These are all essential to enhance the dataset’s quality and the model’s learning efficacy.

Base model selection

Your custom model will be built on top of a base model, so your choice of base model is important. A well-chosen base model ensures that your fine-tuning efforts are efficient and effective, leveraging existing strengths. Selecting the right base model impacts the overall performance, scalability, and relevance of your final AI solution, aligning it more closely with your enterprise's specific needs.

The fine-tuning process

LLMs use “weights” to determine how they process and understand information. These weights influence how the model forms associations between different pieces of knowledge, helping it understand context and relationships. Think of weights as dials that can be adjusted to make the model more accurate.

The fine-tuning process takes a pre-trained base model and trains it further to adjust these weights. This adjustment refines the model's ability to handle specific tasks, making it more relevant and accurate for your enterprise's needs.

The fine-tuning process involves writing a program that loads the base model and the training dataset, prepares some additional steps (such as data cleanup, formatting, tokenization, and collation), and then feeds the training dataset to the model. Common, open-source tools used for fine-tuning are PyTorch torchtune, TensorFlow, and HuggingFace. OpenAI also offers an API for fine-tuning some of its models. 

There are several methods that can be used to fine-tune a model:

1. Full parameter training: Adjust every parameter of the base model. This is very expensive and typically only done for small base models.
2. Transfer learning: Freeze most of the parameters of the base model, and train only the top layer. This is more practical for large base models and generates good results.
3. Parameter-efficient fine-tuning (PEFT): Freeze the entire base model, but add a new, small set of trainable parameters. This method is very effective and gaining popularity. LoRA is an example of a PEFT algorithm.

Validation and testing

When training a model, evaluating its resulting performance is important. For different tasks, there are different metrics and benchmarks. You should always try the trained model on real-world data to ensure synthetic tests don’t give you a false impression of high performance.

Iterate by continually adjusting your fine-tuning parameters and testing the resulting model until you reach acceptable performance.

Key considerations when using in-house data

Enterprises that plan to use in-house data for custom-building their models should consider several key factors:

Data security

Protect the data you use for enterprise AI training with the same policies and security measures that you use in your AI system. Your data is not externally exposed if you run your entire fine-tuning pipeline internally, making this a more protective option. However, if you use a third-party service like OpenAI, then you need to make sure you trust them with your in-house data.

Ethical considerations

AI safety is a major concern for all stakeholders—end users, service providers, and employees. Be careful about the base model you choose, vetting it and its data sources for safety and alignment. Likewise, during the fine-tuning process, paying careful attention to labels and feedback you provide will help to ensure you don’t bias the model.

Regulatory compliance

Data privacy and security compliance is non-negotiable for enterprises. Your fine-tuning data—as well as access to the model and the insights it generates—must adhere to all pertinent rules, regulations, and policies.

Ensure transparency in all processes by providing clear documentation and open communication. Maintain accountability at every stage through rigorous auditing and monitoring practices.

Data quality and relevance

Ultimately, your custom model is only as good as the in-house data you train it on. This means investing time and effort in gathering, preparing, and curating that data to ensure it is relevant to the tasks you intend your model to handle.

Technical feasibility

Building a machine learning pipeline to handle fine-tuning is not trivial. While the hardware resources are not as involved as training a new LLM from scratch, they are still considerable. In addition, you will need skilled engineers and expertise to create and successfully deliver the expected results.

Deployment and monitoring

Once your custom model has been trained to your satisfaction, consider how to deploy it and monitor its performance. Deploy as many instances as necessary to handle requests. This elastic scalability should be coupled with proper monitoring. It is recommended to collect feedback from users post-deployment about the model’s performance. If performance falls below expectations, then it’s time to re-tune your model.

Transform your AI capabilities with in-house data

GenAI applications can benefit immensely from incorporating proprietary in-house data to improve AI model accuracy and relevance. One of the best techniques to incorporate such data is by using fine-tuning to train custom models. The advantages are enhanced domain-specific responses, control over sensitive information, and potentially better performance with smaller models. 

Developing custom models involves data collection and preparation, selecting a base model, fine-tuning, and rigorous validation. However, enterprises going down this route have key considerations to bear in mind: data security, ethical practices, regulatory compliance, and ongoing performance monitoring.

As a business leader and IT decision-maker, your proprietary domain-specific datasets are unique and valuable assets. Integrating those assets into your GenAI applications may not be trivial, but the rewards can be substantial. 

To dive even deeper into the process of LLM training and fine-tuning, check out Training LLMs: An efficient GPU traffic routing mechanism within AI/ML cluster with rail-only connections.