LLM Pre-Training and Custom LLMs

Large language models (LLMs) are a type of generative AI model that has been trained on a massive natural language dataset, using advanced machine learning (ML) algorithms, to learn language and its nuances. LLMs are very effective at language-related tasks such as answering questions, chat, translation, content summarization and generating complex content in response to written prompts. ChatGPT, Claude, Gemini, Microsoft Copilot and Meta AI are all examples of LLMs.

The process of training LLMs to serve the needs of enterprises may include pre-training, custom data training and fine-tuning.

Exploring LLM Pre-Training

Pre-training is the process of training an LLM to have a general understanding of language, including linguistic patterns, grammar, syntax and semantics. This is accomplished by feeding the model a huge dataset of a wide variety of texts from sources such as books, articles, websites and social media.

The Importance of Pre-Training

Pre-training gives LLMs broad language capabilities, allowing them to process and generate language across various applications and tasks. This builds a strong foundation for performance while enabling LLMs to adapt to more specialized tasks with further training.

Pre-Training Process

LLM pre-training involves multiple steps to ensure that the model can effectively understand and generate human-like text.

Collecting and Processing Data

LLMs require an extensive, varied dataset for pre-training. Raw data used for pre-training must be cleaned and preprocessed before being fed to models. This involves removing noise such as duplicate entries and non-textual elements, formatting issues and irrelevant information. AI-driven algorithms can be used to automatically clean and filter data.

Tokenization

Cleaned data is then tokenized, or converted into a numerical format. Tokenization breaks down the cleaned text into smaller units, such as words, subwords or characters and maps them to unique numerical tokens. These form the input sequences required for training the model.

Training

Once data is tokenized, large datasets are fed into the neural network to help it learn patterns. The core purpose of pre-training is teaching the LLM how to predict which word is most likely to come next in a sequence. Foundational models use unannotated datasets with self-supervised learning. In this method, the model learns by predicting “missing” data using other parts of the same data as a form of supervision, without relying on human-labeled data.

LLMs often use the transformer type of neural network architecture. Transformers have an “attention” mechanism that allows them to figure out how parts of the input influence other parts. This makes them ideal for natural language processing (NLP).

Testing and evaluation

After training, the model is evaluated for accurate responses and further trained until the desired level of performance is reached and the model can be deployed.

Training LLMs with Custom Data

Pre-training results in a generic foundational model. Often, such a model must be further trained to make it useful for specific tasks, domains and applications using an organization’s own custom data.

In some cases, an organization may wish to train an LLM from scratch using all custom data rather than starting with a pre-trained model, but this requires extensive proprietary data, time and resources. More often, organizations customize pre-trained models via fine-tuning.

Benefits of Custom Data Training

Using custom data to train an LLM offers several benefits:

• Relevance and accuracy: Custom data helps the model better understand and generate content related to a particular field or industry, leading to more accurate outputs that are relevant for users.
• Data Privacy and Security: Custom LLMs give organizations control over their data. They can keep data within the organization, protecting against data breaches, exposure of sensitive data or IP and compliance issues. This is particularly important in industries dependent on personal data, such as healthcare and finance.
• Control Over Model Behavior: Businesses can train model behavior to align with their unique rules, values or compliance standards. This is essential in regulated sectors where organizations need to ensure compliance.
• Hallucination Reduction: Domain-specific data helps prevent the generation of incorrect or fabricated information. Custom LLMs are grounded in relevant facts, improving the reliability of their outputs.
• User Experience: A model trained with custom data provides outputs that are tailored for users. Focused training reduces reliance on broad knowledge, allowing custom models to deliver faster responses for domain-specific questions, enhancing the user experience.
• Competitive Advantage: A custom-trained LLM can offer capabilities specifically tailored to domain-specific tasks and market demands, helping companies innovate quickly, provide better services and differentiate themselves from competitors. For example, a financial services company could train an LLM on regulatory documents, market analysis reports and trading data to create a model that excels at risk assessment compliance monitoring and investment research–capabilities that would give them a significant edge over competitors using generic models.
• Scalability and Flexibility: Custom LLMs are easier to scale and adapt as business needs evolve. Organizations can modify parameters or integrate the model into tailored workflows without being restricted by third-party limitations.

Examples of Customized LLMs

Customized LLMs can be tailored to specific industries, organizations or use cases by training them on domain-specific data. Prominent examples include:

• BloombergGPT: This model was trained on a combination of public data and Bloomberg’s proprietary financial datasets to support tasks such as market analysis, risk modeling and automating financial research.
• MaaS (Model-as-a-Service): Morgan Stanley built this custom LLM using OpenAI infrastructure, trained on the company’s internal dataset of financial documents, investment strategies and research. It helps advisors quickly access firm-specific knowledge.
• Med-PaLM 2: This model, developed by Google and fine-tuned on medical exams, research and clinical data, is designed to answer medical questions and has reached expert-level accuracy in some cases.
• GE Vernova: Trained on technical manuals, sensor data and diagnostic logs, this LLM supports energy infrastructure and maintenance by assisting engineers with troubleshooting and system optimization.

Fine-Tuning LLMs

LLM fine-tuning is the process of taking a pre-trained LLM and further training it on a specific dataset to tailor its behavior for a particular task, domain or application. Unlike training a model from scratch, fine-tuning builds on the model’s existing linguistic and contextual knowledge while guiding it to adapt to new requirements. Fine-tuning enables a model to go beyond general-purpose utility, aligning a model’s outputs with an organization’s unique goals, language and data environments. Fine-tuning can significantly improve the accuracy, relevance and safety of a model’s responses in real-world applications.

Fine-Tuning Methods

A variety of methods can be used for fine-tuning LLMs. Some of the most common include:

• Supervised Fine-Tuning: In this method, pre-trained models are further trained using smaller, labeled datasets that have been validated. This helps the model learn specific tasks more accurately by adjusting its parameters based on the provided examples.
• Instruction-Tuning: Instruction-tuning is a supervised fine-tuning technique that trains an LLM to understand and follow specific task instructions. This technique uses relevant examples that demonstrate how the model should respond to a query, improving a model’s ability to follow human commands.
• Transfer Learning: This supervised fine-tuning technique enables learning on specific tasks by training a small subset of the model parameters. It’s useful in situations where there is limited labeled data available for training on specific tasks.
• Few-Shot learning: Another supervised fine-tuning technique, few-shot learning enables LLMs to adapt to new tasks with minimal labeled examples. The model is trained to perform a task after being exposed to only a few examples, enabling it to generalize its understanding.
• Reinforcement Learning from Human Feedback (RLHF): This method trains LLM behavior with human-labeled feedback, ensuring its responses align with user preferences and ethical considerations. This approach enables the model to continuously evolve using real-world information.

Data Preparation for Efficient LLM Training

Data quality is critical for training LLMs, directly impacting a model’s accuracy, efficiency and fairness. High-quality data provides a clean, rich foundation for models to learn from, enabling them to effectively and precisely meet organizations’ needs. A well-curated dataset streamlines the fine-tuning process and reduces resource consumption while improving output relevance and accuracy for optimal performance.

Key data quality considerations

A number of key considerations go into selecting and preparing data for LLM training, including:

• Volume: The volume of data needs to be high enough for effective training — although quality is more important than quantity. Smaller datasets that are clean, diverse and focused on the task often yield better performance than large, noisy datasets.
• Relevance: Relevant data ensures the model is fine-tuned with contextually appropriate information.
• Diversity: Diverse datasets help avoid overtraining a model on a specific type of response. This can bias the model toward giving that response even when it’s not the most appropriate answer.

Preparing High-Quality Data

The process of data preparation for LLM training begins by identifying quality data sources that meet key considerations and align with the intended use case. Subsequent steps include:

Data cleaning is a core part of the preparation process. This includes removing duplicates, correcting typos, filtering out irrelevant or low-quality entries and standardizing formatting. Clean, consistent data improves training efficiency and reduces the likelihood of errors or misleading outputs.

Addressing bias in datasets is critical for fair, balanced outputs. Bias, including gender, race and cultural bias, can be introduced through historical data, uneven sampling or flawed labeling processes. To mitigate this, datasets should be reviewed for imbalances in representation. Using a variety of sources and applying fairness auditing tools can help promote equity in model outputs.

Privacy and security considerations are non-negotiable for compliance with legal and regulatory requirements and protecting an organization and individuals from data misuse. Any dataset containing sensitive or personally identifiable information (PII) must be anonymized or scrubbed. Organizations should implement strict data handling protocols and ethical collection practices, including obtaining consent and respecting user confidentiality.

Optimizing LLM Performance

LLM learning doesn’t end with pre-training, fine-tuning or other custom training. Organizations must update and optimize LLMs with techniques to improve accuracy and overall performance. This process should be a continuous cycle of assessment, training and monitoring.

Evaluating performance

The first step in optimizing an LLM is evaluation. This involves testing the model using annotated datasets, ground truth comparisons and real-world scenarios. User feedback and tools such as error analysis and comparisons against pre-trained baselines can also help determine which improvements need to be made.

The evaluation process pinpoints areas where the model excels and where it may fall short, measured against pre-identified metrics. These metrics can include areas such as relevance to business objectives, accuracy, bias, compliance or other factors aligned with the organization’s goals for the LLM.

Iterate and Improve

Once desired improvements are identified, the model can be refined with new data. A common technique is hyperparameter tuning, which involves adjusting learning rates, batch sizes or training epochs to find the right balance for optimal performance.

Another method is Retrieval-Augmented Generation (RAG), which leverages custom data to enhance factual accuracy using real-time information. RAG ensures that LLMs are working with up-to-date information and is crucial for scenarios where facts change over time.

Each iteration should be informed by performance metrics and practical outcomes, ensuring that the model not only improves but remains aligned with its defined purpose.

Monitor and maintain

Active monitoring is crucial to maintain an LLM’s relevance and effectiveness. This includes tracking key performance indicators (KPIs), detecting model drift and refreshing training data as the domain evolves. Regular maintenance should also address changes in user behavior, regulations or business strategy, and may involve retraining or further fine-tuning of the model. Establishing a monitoring pipeline that includes logging, alerting and periodic evaluations ensures that the model continues to deliver accurate and fair results over time.

Data-centric AI with Databricks

The ability to manage AI models has become critical for enterprises to stay competitive. Mosaic AI, part of the Databricks Data Intelligence Platform, unifies data, model training and production environments in a single solution. This allows organizations to securely use enterprise data to augment, fine-tune or build their LLMs. With Mosaic AI, organizations can securely and cost-effectively build production-quality AI systems, centrally deploy and govern all AI models and monitor data, features and AI models in one place.