The Importance of Training Data in Different Machine Learning Techniques for AI
Generative AI models have exhibited remarkable capability to process, understand, and generate human-level text. However, in 2024, a McKinsey survey found that 63 percent of respondents faced the greatest risk in output inaccuracy they saw in their organizations while using gen AI models. The model output accuracy significantly depends on the data it learns from. In other words, training data plays a significant role in developing a high-accuracy model.
Data collection has three lead metrics that every gen AI needs to fulfill: quality, diversity, and
quantity. High-quality data produces more accurate and coherent text, diversity in the dataset
covers a broader range of topics and styles, and an ample quantity of training data contributes to the model’s overall proficiency.
This article delves into the role of training data across different machine learning techniques — pretraining, supervised, unsupervised, and semi-supervised learning.
Pretraining
Pretraining is the preliminary and basic step in developing generative AI models. In this step, the model learns general language understanding from a vast corpus of unlabeled datasets. Pretraining helps a model learn various data structures and patterns, giving it the power to suggest the next words or sequences easily.
Generative AI models are fed with terabytes of data sourced from different resources like e-books, online websites, articles, images, etc., enhancing their ability to understand complex language structures, like rare words, idiomatic expressions, and domain-specific jargon. The quantity, quality, and variety of training data are critical to developing a broad understanding of language, which lays the foundation for the following stages.
Supervised Learning
Supervised learning employs labeled data to train algorithms to recognize patterns and make predictions. It helps build sophisticated models that can make accurate predictions. These models can be used across industries, such as healthcare, retail, financial services, etc.
Supervised learning requires human annotation and feedback to detect, flag, and remove errors, improving the model’s performance and reliability in real-world scenarios.
Unsupervised learning
The unsupervised learning technique uses ML algorithms to analyze and group raw unlabeled datasets. The algorithms identify hidden patterns, structures or relationships within data without the need for human intervention.
Semi-supervised learning
Semi-supervised learning is a technique that combines elements of supervised and unsupervised learning. It works with a small amount of labeled data and a larger amount of unlabeled data. The labeled data guides the learning process, while the algorithm learns more general patterns and representations from unlabeled data.
One-shot, few-shot, and many-shot learning are specialized methods used in semi-supervised learning to address the challenge of learning from limited labeled data.
Human-in-the-Loop in Supervised Training Process
As shown below, humans play an indispensable role in a supervised training process.
Collection of Raw Data: Raw data collected from a variety of sources is used at the beginning of most supervised learning due to scarcity of prelabeled datasets.
Data Annotation: The raw data is labeled to highlight the essential elements that the machine learning model learns from. Human annotators and data scientists curate and label data.
Model Ingestion: The annotated data is fed into the model, which isolates and processes the desired elements. The larger learning process is automated but resource-intensive and time-consuming.
Model output: The test data is used to check the accuracy of the trained model’s predictions to validate its performance. The model that produces the correct output is finalized for deployment. Otherwise human evaluators provide feedback to the model about its decision-making capability, detect and address training data issues and further optimize and fine-tune the model using additional training.
AI Training vs Testing Data
A trained AI model is evaluated for accuracy and performance through testing. The test data is used to ensure the model’s ability to deliver output within a desirable range of precision and overall reliability.
In supervised learning, labeled training data enables an AI model to identify and analyze patterns or relationships, while testing data is input in a raw form, which the model has not seen previously. The data is not labeled to guide the model in unsupervised learning methods. The model has to identify the structure within the data on its own.
The test data evaluates the model’s generalizability beyond specific examples it encountered during training.
Conclusion
Training data is the main force that pushes and drives the Generative AI models towards
evolution with different stages of this process. The quality and diversity of training data play a
critical role in defining the success and usefulness of generative AI models. The pretraining phase uses vast and diverse datasets, while the RLHF process utilizes human-annotated data.
Reward modeling helps model data align with the human-preferred outputs. With such advanced fields, AI will evolve in the future with careful curation and data labeling. It will make all the outcomes of Generative AI models accurate and aligned with human values and the capabilities to handle increasingly complex tasks.
