Schedule a Consult
Back to Blogs

What You Should Know About Training an LLM on Custom Data

April 15, 2024

Large Language Models (LLMs) have taken the world by storm, showcasing remarkable capabilities in language understanding and generation. However, their true potential often lies in tailoring them to specific domains and tasks through custom training. Let’s review the technical intricacies of training LLMs on custom data.

Pre-training

Custom LLM training doesn't begin from scratch. Pre-trained models serve as a foundation, having already learned intricate language patterns from massive datasets. This pre-trained knowledge reduces training time and computational resources compared to building an LLM entirely from scratch.

2. Data Acquisition: The Fuel for Learning

The heart of custom LLM training lies in the data you provide. Here's what you need to consider:

  • Relevance: The data must be highly relevant to the specific domain or task you want the LLM to excel in. Irrelevant data can hinder performance and introduce biases.
  • Quality: High-quality data is crucial. Ensure your data is clean, free of errors, and well-formatted for the chosen LLM architecture. Tools like data cleaning and validation techniques are essential.
  • Quantity: While more data is generally better, the amount required depends on the complexity of the task and the chosen model. Extensive datasets can lead to better performance, but also require more computational resources for training.

3. Preprocessing the Data

Before feeding the data to the LLM, it needs to be preprocessed. This involves:

  • Tokenization: Breaking down text into smaller units like words or sub-words, which the LLM can understand and process.
  • Normalization: Bringing the data into a consistent format, such as lowercase conversion or stemming/lemmatization (reducing words to their root form).
  • Handling imbalanced data: Addressing situations where certain categories are overrepresented in the data, which can skew the model's learning. Techniques like oversampling or undersampling can be employed.

Fine-tuning: Tailoring the Pre-trained Model

Fine-tuning is where the magic happens. It involves adjusting the pre-trained model's parameters based on your custom data. This allows the LLM to specialize in the specific domain or task at hand. Here are the two main approaches:

  • Full Fine-tuning: This involves retraining the entire pre-trained model on your custom data. This approach requires a significant amount of data and computational resources, but can lead to the most significant performance improvements.
  • Adapter-based Fine-tuning: This method introduces a smaller module (adapter) on top of the pre-trained model. The adapter learns to adapt the pre-trained knowledge to the specific task, requiring less data and computational resources compared to full fine-tuning.

Training and Evaluation

Training involves feeding the preprocessed data to the chosen fine-tuning approach. Monitoring the training process through metrics like loss function and validation accuracy is also important.

Evaluation involves assessing the LLM's performance on unseen data. This helps determine if the fine-tuning process has been successful and identifies areas for further improvement.

Remember: Training LLMs can be computationally expensive, so consider factors like available resources, desired performance level, and the complexity of your task when choosing a training approach.

Choosing the Right Tools for the Job

  • Open-source libraries: Popular options like Hugging Face Transformers and OpenAI Gym provide pre-trained models, fine-tuning techniques, and training utilities.
  • Cloud platforms: Cloud providers like Google AI Platform and Amazon SageMaker offer managed services for LLM training, simplifying infrastructure management and resource allocation.
  • Hardware accelerators: Utilizing GPUs or TPUs can significantly speed up the training process, especially for large models and complex tasks.

8. Addressing Challenges in Training LLMs on Custom Data

Custom LLM training presents several challenges:

  • Data scarcity: Often, obtaining sufficient high-quality data can be a significant obstacle. Exploring data augmentation techniques and transfer learning can help mitigate this challenge.
  • Computational cost: Training LLMs can be computationally expensive, requiring powerful hardware and potentially leading to high costs. Exploring cloud-based training services or resource-efficient training techniques can offer solutions.
  • Interpretability and bias: Understanding how the LLM arrives at its outputs and mitigating potential biases in the data and training process are crucial aspects of responsible development. Techniques like interpretability methods and fairness considerations are essential for building trustworthy custom LLMs.

Unleash the Full Potential of Your Custom LLM with Sapien

The success of custom LLM training requires:

  • High-quality data: Ensure your data is accurate, unbiased, and relevant to your specific task.
  • Interpretability and bias mitigation: Understand how your LLM arrives at its outputs and actively address potential biases in your data and training process.
  • Scalability and efficiency: Manage the computational demands of training and adapt your approach to your project's needs.

Sapien's human-in-the-loop data labeling platform allows you to address these challenges and unlock the full potential of your custom LLM. We offer a suite of data labeling services with targeted data audits, real-world grounding, and bias mitigation efforts to ensure your LLM is trained on accurate and representative data.

We employ explainability methods to help you understand your LLM's reasoning and identify areas for improvement, and our continuous fine-tuning process and flexible, scalable labeling solutions ensure your LLM training is efficient and cost-effective.

Partner with Sapien to:

  • Access a global network of skilled data labelers: Our diverse workforce possesses the expertise to handle various data types and labeling tasks.
  • Benefit from stringent data security protocols: We safeguard your data with security measures and multi-stage quality assurance processes.
  • Enjoy a custom-tailored approach: We collaborate with you to design a data labeling workflow that aligns perfectly with your specific use case and quality requirements.

Whether you're starting your first custom LLM training project or seeking to refine an existing model, Sapien empowers your LLMS. Let's discuss how our data labeling solutions can help you build powerful, accurate, and trustworthy LLMs and train LLMs on custom data.