Mind the Gaps

Unravelling the Critical Role of Structured Data in Private Large Language Models

The demand for large language models has skyrocketed in the era of artificial intelligence and machine learning. These models, such as GPT-3.5, have demonstrated their potential in various applications, from chatbots to content generation. However, one often overlooked aspect of building these models is the quality and structure of the data used to train them. As the saying goes, "rubbish in, rubbish out," and this principle holds true when it comes to training private large language models.

Data Quality Matters

Before delving into the importance of structured data, let's first address the significance of data quality. Building a robust and effective language model requires a vast amount of data. However, having a large volume of data is not enough. The data must be accurate, reliable, and representative of the language patterns and knowledge you want the model to acquire.

For instance, consider a scenario where a language model is being trained on unstructured social media data. While this data may be abundant, it often contains errors, biases, and colloquial language that may not align with the desired output. This can lead to the model producing biased or inappropriate responses, seriously affecting real-world applications.

Data Relevance is Pivotal

Using outdated data to train a language model can be as detrimental as using unreliable information. Language evolves, contexts change, and societal norms shift. Failing to keep your training data current can lead to inaccuracies, misunderstandings, and ethical concerns. To build an effective and responsible large language model, prioritize data relevance by regularly updating your training data to reflect the ever-changing world accurately.

Structured Data Enables Control

Structured data provides a clear advantage in terms of control over the training process. When data is structured, it's easier to curate, clean, and preprocess. This means you can more effectively identify and address potential issues, such as biases or inaccuracies.

For example, imagine you are building a large language model for medical diagnosis. Structured medical records with standardized formats allow you to maintain data consistency and ensure that the model learns from reliable sources. You can easily filter out irrelevant information and focus on the specific medical knowledge needed.

The Role of Supervised Learning

Supervised learning is a common approach for training large language models. In supervised learning, structured data with labelled examples is crucial. These labels act as ground truth, helping the model understand the correct associations between inputs and outputs.

Take the example of training a language model to perform sentiment analysis on customer reviews. Structured data containing text reviews paired with corresponding sentiment labels (e.g., positive, negative, neutral) enable the model to accurately learn the relationships between textual patterns and sentiment expressions.

Structured Data for Fine-Tuning

Fine-tuning is an essential step in training large language models. It allows you to adapt a pre-trained model to your specific task or domain. Structured data is particularly valuable during this phase, as it facilitates fine-grained control over the model's behaviour.

For instance, if you're building a language model for legal document analysis, structured legal documents (e.g., contracts, court cases. etc.) can serve as excellent training data. This structured content helps the model grasp the nuances of legal language and context.

It’s All coming back to the quality of the data

Data is undeniably the foundation in the quest to build private large language models. However, the quality and structure of that data are equally, if not more, important. The adage "rubbish in, rubbish out" resonates strongly in the world of machine learning, where the training data profoundly influences a model's performance. To achieve the desired results and maintain ethical standards, prioritize structured data that aligns with your model's intended purpose. Working closely with consultants or Data Scientists will help you make better decisions on the architecture for the LLM as well as having more confidence in the project's outputs to ensure that they make meaningful advances for the business. By doing so, you can harness the true power of large language models while minimizing the risks associated with unstructured or low-quality data.