How to Train an LLM With Custom Data

Should You Train an LLM With Custom Data?

Training an LLM with custom data is a good idea if you’re trying to automate end-to-end tasks, if you need domain-specific expertise, or if you’re trying to automate complex work.

Caktus.ai is a great example because they used 3 specialist models to downstream academic tasks and expand their product offerings. As Caktus moved away from the generic GPT-3 model to the Generative AI model specialized for academia, their process wasn’t hindered anymore. They were able to excel and make one of the best academic AI models.

Training a large language model with custom data helps:

• Provide domain-specific expertise
• Tailor predictions or actions to a specific task
• Improve accuracy and performance
• Enhance user experience
• Improve data security and privacy
• Offer competitive advantage
• Reduce costs
• Improve scalability while making it more affordable

Training an LLM with custom data helps create a tailored approach to increasing the company’s competitiveness in the market, as well as address unique challenges and leverage proprietary data.

Partnering With a Data Provider vs. Using Custom Data

If you don’t have enough custom data or just want additional vetted data, you can partner with a data provider.

Carefully selected data that fits your needs can help you expand the products, information, and value you can provide with an LLM.

For example, we helped Caktus fine-tune an LLM using research papers from CORE—the world’s largest collection of open-access research papers.

That way, we turned a generic model into an advanced AI solution, allowing Caktus to offer a premium product with proprietary technology, a higher quality experience, and better outputs to users.

We should note, however, that the fine-tuning process was preceded by complex data engineering.

To turn CORE’s research papers into a suitable-for-training, custom dataset, we took the following steps:

1. Adjusted the data
2. Extracted relevant data
3. Performed OCR
4. Structured the data
5. Cleaned the data
6. Normalized the data

What Is an Advantage of a Company Using Its Own Data to Customize an LLM?

Leveraging your custom data to customize an LLM helps significantly improve the model’s accuracy and relevance.  This leads to better model performance, enhanced customer experience, and strategic advantages within your industry.

Using your data to customize an LLM can help with:

• Precision - model gains a deep understanding of industry-specific terminology and practices, which helps generate accurate and relevant outputs.
• Expertise - the model becomes proficient in the company’s specific domain, which helps it handle queries and tasks with a higher level of expertise which an off-the-shelf general model can’t achieve.
• Personalization - the company’s custom data helps the model to understand and anticipate customer needs, preferences, and behaviors, leading to personalized and effective interactions.
• Optimization - custom data helps the model excel at specific tasks that are relevant to the company’s operations.
• Capabilities - unique capabilities tailored to the company’s strategic goals help set it apart from competitors who rely on more generic models.
• Resource optimization - Custom data helps the company focus training resources on the most relevant data, reducing the time and computational power required for training.
• Reduces post-processing - Using custom data can help align the model with company-specific needs, reducing post-processing and manual adjustment.

Customizing an LLM with company-specific data ensures better brand alignment and consistency, more valuable insights, predictive analytics, and a better decision-making and strategic planning process.

“Building on your infrastructure with your data never leaving your walls is going to be the most secure way.” — Ankur Patel

Improved Relevance

Using the company’s data always helps improve relevance. LLM can produce content relevant to your specific domain, increasing the accuracy of the output.

If there’s any industry-specific terminology, LLM can learn and adapt, compared to a generic pre trained model which handles jargon and terminology well.

Better Performance

Custom training data can also enhance the model’s performance on specialized tasks like customer support, content generation, or technical writing.

LLM trained on custom data can understand the context from prior interactions, which leads to more coherent and contextually appropriate responses.

Reduced Risk

Training on your proprietary data allows you to keep sensitive and confidential information in-house, which helps reduce the risk of breaches.

At the same time, it gives you a competitive advantage. Unique AI solutions trained on your custom data can provide a great amount of unique capabilities.

Benefits of Having Proprietary Technology

That’s one of the things we’ve learned while working with Cacktus, where we built one of the world’s best academic AI models. Tao Zhang, the CTO and co-founder of Caktus, said that professional investors don’t like to participate in companies that lack proprietary technology since they expect the market to become too competitive.

Training a large language model on custom data offers differentiation, which gives the company a competitive advantage with the benefits of improved performance.

How to Prepare Your Data for LLM Training

Preparing your training data for LLM is a crucial step that ensures better accuracy. High-quality data is a requirement, and the preparation often happens in a three-step process:

1. Data cleaning
2. Data labeling
3. Data validation

Data preparation includes processes like removing errors, duplicates, or unnecessary information, dividing data into training and validation sets, and ensuring the text is clear and correctly formatted.

Data Cleaning

Data cleaning consists of duplicate removal, handling missing values, standardizing formats, correcting inaccurate data, and removing outliers.

To do this, you start off with finding and removing duplicates in a dataset, from where you can then detect missing values and decide on a strategy to handle them. Data with missing values can be removed or assigned the missing values.

The next step is ensuring that all data follows a consistent format, especially dates, numbers, and categorical variables. If there is inaccuracy in data, this is also the step of the process where you identify and correct inconsistencies.

Lastly, removing outliers that may distort analysis is a final step before moving on to data labeling. This is best done by using statistical methods to identify outliers. You can decide on a case-by-case basis whether to remove or correct the outliers.

Data Labeling

The first step of data labeling is clearly defining what each label presents in your dataset. In this step of the process, you can use domain knowledge to create meaningful labels that are relevant to your company or industry.

There are two ways to label data: automatically and manually. It’s best to rely on both methods and to start off with an automated approach.

Automated labeling relies on tools and algorithms to label data when possible, which is highly recommended for simple data. Complex data requires manual labeling which can’t be labeled by automated methods.

After the labeling process is complete, it’s important to implement quality control measures to ensure labels are accurate.

Data Validation

When your data is clean and labeled, validating helps ensure all data fits the expected data types. One of the best ways to validate data is by using type-checking functions and data validation libraries.

It’s important to validate the numerical data falls within expected ranges and adheres to business rules. At the same time, it’s also important to ensure all foreign key references in your data are valid.

Consistency checks and statistical validation are the last steps which help ensure the data is logically consistent.

Data preparation is time-consuming, but very necessary. As our founder Ankur mentions, it’s better to have less high-quality data than loads of unstructured and unlabeled data.

Our lead ML engineer agrees and has shared his opinion based on first-hand experience:

“Another problem we sometimes encounter is low-quality data. We work closely with clients in the initial stages of the project and tell them exactly what we need. We can easily recognize if their data is not that great. From there, we work with them to improve or find better data.” — Andrew McKishnie

If you rely on data from dataset partners, preparing such data for model training can be more complex and require data engineering. This can involve the 6-step process we mentioned earlier in the post.

How to Train and Fine Tune an LLM With Custom Training Data

Finally, your data is ready for LLM training.

This process involves:

1. Choosing a pre-trained LLM
2. Setting up the environment
3. Fine-tuning the model
4. Monitoring and evaluating
5. Testing the model
6. Deploying and using a model

Pre-trained LLMs are trained on large amounts of general text, so it’s wise to choose a model similar to the industry or specific tasks the company handles.

The training environment includes tools and platforms that provide easy ways to fine tune a base model. One of the most well-known such environments is OpenAI's fine-tuning API.

Fine-tuning LLM models consists of uploading clean and organized data into the tool and platform. The process trains the base model on specific data, teaching it to produce outputs that match the company’s needs.

After fine-tuning, it’s important to monitor the model’s performance using the validation set to ensure correct machine learning. This is the step where you can adjust the data (if needed) or fine tune the settings to improve performance and accuracy.

When testing a model, it’s important to use new and unseen examples to ensure it performs well in real-world scenarios. Once satisfied with the performance, a model is deployed to the company’s applications.

Monitoring the model’s performance and updating it with new data, is a great way to keep it effective. However, new data updates aren’t always necessary.

These simple steps help customize an LLM understand better and generate outputs that suit the company’s specific needs.

Let Us Build You an LLM With Your Data

Would you like to train large language models using your data and experience the benefits of having a proprietary AI solution? Please schedule a 30-minute call with our experts.

We can discuss your needs, understand your data, and find the best way to train an LLM using your data. We can also show you how our AI solutions work live, so book a call today!