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From chaos to creation: How data labeling drives success in generative AI

From chaos to creation: How data labeling drives success in generative AI

Training data is the cornerstone of AI algorithms – the quality of outputs is contingent on the data the AI model was trained on. The data determines the success of AI models, underscoring the importance of data labeling.

Data labeling plays a crucial role in generative AI by providing context and meaning to the data used for training machine learning algorithms, enabling them to generate more meaningful outputs. For example, ChatGPT was trained on both labeled and unlabeled datasets. The labeled data included over 160,000 dialogues between human participants.

Let’s discover the power of data labeling in generative AI.

What is data labeling

Data labeling involves identifying objects within raw digital data (images, text files, videos, etc.) and adding informative labels or tags to them to enable AI models to make accurate predictions and assessments. In other words, AI/ML models learn the context from labeled data. For example, labels identify and tag a dog or cat in a photo, words uttered in an audio recording, or a tumor in a CT scan. Data labeling has a wide range of applications across industries, some of the most common use cases include, computer vision, natural language processing (NLP), and speech recognition

Data labeling for generative AI

The emergence of large language models and generative AI has significantly increased the demand for quality data. Most machine learning models use supervised learning, which involves using an algorithm to map inputs to desired outputs. For supervised learning to work, you need a dataset with predefined labels that the model can learn from to make predictions and correct decisions. The algorithm learns from the labels, allowing it to evaluate its accuracy and improve over time.

Generative AI and large language models (LLMs) are trained on vast amounts of data, providing them with a broad knowledge base stored in their pre-trained weights. However, they might still struggle with specific problems due to a lack of focused information. This is where data labeling comes in.

Fine-tuning LLMs has become an important step in training them to generate creative content or translate languages. The process involves using labeled datasets specifically designed for instruction tuning to further train publicly available LLMs like GPT-3. Let’s look at the significant roles of data labeling in generative AI.

Quality optimization: Data labeling drives up the quality and accuracy of the training data. Annotators meticulously categorize different scenarios within the data, ensuring AI models learn effectively from accurate information.

Semantic understanding: Generative AI models need to understand the context and meaning of the raw data they learn from to create outputs that are more accurate, coherent, and relevant. Data labeling provides context and meaning for the training data, allowing models to develop a deeper semantic understanding and generate outputs that make sense in the context.

Supervised learning: In supervised learning, labeled data are used to train models to figure out the correct outputs for specific inputs. Data labeling gives models the instructions on type of output expected, helping them deliver the desired outcomes.

Biased mitigation: Data labeling helps fight bias in generative AI models. Biases surface due to limited and narrow data representing a particular group. Data labeling allows for more control over the information the generative AI model is trained on. By using carefully curated and labeled data that represents a wide range of perspectives, situations, and people, we can guide the model for a balanced understanding

Type of data annotation for generative AI

Various data annotation methods are used for generative AI. Each technique involves labeling data with specific attributes or features, enabling the models to learn underlying patterns and relationships with the data and create new content.

Image annotation: It is the process of adding descriptive tags or labels to objects or people in an image.

Entity recognition: It involves identifying and labeling important keywords or phrases within a text, such as identifying names, locations, or organizations (eg. Albert Einstein, London, Google).

Sentiment analysis: This method focuses on understanding emotions or sentiments in a piece of text data and assigning labels, such as positive, negative, or neutral (fantastic, awful, or indifferent)

Metadata annotation: Extra information is added to raw data for context, assisting generative AI models in understanding the data in its broader context for more accurate analysis and interpretation. This includes details like location data, author information, timestamps, image source, and other relevant details that help the model to better understand the context of the data.

Conversation categorization: It focuses on classifying text data into different categories based on its topic or purpose, such as general inquiries, sales discussions, or customer complaints. This type of labeling helps AI models interpret the overall goal of the conversation and respond appropriately.

Final words

Data labeling empowers generative AI models to achieve superior performance by enabling them to generate more accurate and meaningful outputs suited to specific goals. Major AI companies, including OpenAI and Meta, have reportedly hired hundreds or thousands of human labelers to handle the massive amounts of data needed for fine-tuning ChatGPT and Llama 2, respectively. This underscores the importance of data labeling in advancing generative AI.