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Paving the Way for Generative AI Excellence Through Data Optimisation
Generative AI is revolutionising the way we approach problem-solving and creativity, from art generation to complex data analysis. The foundation of this technological marvel lies in the quality and organisation of the data it learns from.
In this article, we explore the crucial steps for getting your data in order, the importance of this process, and provide real-world examples and research references that highlight the impact of data quality on AI’s success.
Training data refers to the datasets used to train a machine learning model. In the context of Generative AI, this data is the foundation upon which the AI learns to generate new content or make predictions.
Chief Executive Officer, Decision Inc. Australia
Training data can be classified into several types:
This includes datasets that are highly organized, like tables in databases, where the relationship between different variables is clear.
This encompasses more complex data like text, images, and audio, where the structure is not predefined.
A blend of structured and unstructured data, such as JSON or XML files.
The trick with training data is it has two layers, and both need to be understood. The first layer is the ‘proprietary’ training data of every large language model. Consider this the information the ‘digital brain’ of the language model required for its basic intellect and it is sometimes referred to as the ‘corpus academia’ or the like. The larger the volume of data and the higher the quality, the better the model. Think Leonardo da Vinci (1452 – 1519), famous for his depth and breadth of reading and content creation.
The development of GPT-3 and subsequent model versions, the state-of-the-art language processing AI, illustrates the importance of a vast and diverse dataset for training. Its ability to generate human-like text is a testament to the quality of its training data.*
The second potential training data type is used to customise a model for an express purpose. Think of this as learning a foreign language, the data required for the ‘digital brain’ to comprehend French or Italian. This second area is what we focus on in this article because this is where most effort is underway to make Generative AI solutions specific to relevant use cases.
The definition of Generative AI data quality includes and stretches beyond traditional data quality and governance requirements.
We have three examples of many to be considered:
1. Quality of Generated Content
The quality and diversity of training data directly influence the AI’s ability to generate realistic and varied outputs.
2. Understanding Context and Nuance
Especially in language models and image generators, nuanced and context-rich training data help the AI in understanding and replicating complex patterns.
3. Adaptability and Flexibility
Diverse training datasets enable the AI to adapt to a wide range of scenarios and applications, making it more flexible and versatile.
We believe the field of synthetic data will become increasingly relevant as we ‘unbelievably’ start to run out of training data – content created by humans. Synthetic data is machine created and examples exist in market of successfully use for new language models like the recently released Microsoft open source – Orca 2. But this is a topic for another time!
Once you have assembled a comprehensive, diverse, and relevant dataset including sourcing data from various reliable platforms and ensuring it represents a wide spectrum of scenarios, some important preparation steps are required.
Data Cleaning and Preprocessing
Removing inaccuracies, inconsistencies, and irrelevant information from your dataset. Techniques like normalisation, transformation, and dealing with missing values are essential.
For supervised learning models, accurately labelling the data is critical. This process involves tagging data with relevant labels that the AI can learn from.
Expanding the dataset by creating modified copies of data points. This enhances the diversity and size of the dataset, leading to more robust AI models.
Data Privacy and Ethical Conditions
Ensuring compliance with data protection laws and ethical guidelines is vital for responsible AI development.
These practical steps require expertise, patience and a well-considered approach contextually correct for the nuances of Generative AI technology and will ensure you achieve model accuracy, reduced bias and allow optimisation of model training, performance, and scalability.
The journey towards achieving generative AI excellence is largely contingent on the quality and organisation of the data it is trained on. By focusing on meticulous data preparation, we can unlock the full potential of AI and pave the way for groundbreaking innovations across various sectors.
*OpenAI. (2020). “GPT-3: Language Models are Few-Shot Learners.”
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