Beyond the Algorithm: How Training Data Can Make or Break a Generative AI Model

High-quality training data acts as the lifeblood of or fuel for any generative AI model, empowering them to generate human-like text, image, audio, and video content in response to prompts. ChatGPT and other tools like it extrapolate from its training data to produce realistic content. Vast, diverse, and relevant datasets that generative AI models are trained on significantly impact their ability to generate unique, accurate, and unbiased results because models are exposed to an array of patterns and variations in the data. For example, ChatGPT-4 - the latest version of ChatGPT - is trained on a dataset containing ~13T tokens, including both text-based and code-based data.

This piece of writing explores the importance of training data, the consequences of low-quality data, and how to source quality data for your generative AI model.

Role of data in generative AI models

Training data refers to a dataset that is used to train an AI model to learn patterns or make decisions. Training datasets may include texts, images, audio, and videos. Algorithms are fed with training data to identify trends and correlations that they need to make predictions. The output is only as good as the quality of the data the model learns from.

The quality, quantity, and diversity of training data are of considerable importance to the development and effectiveness of generative AI models, as datasets provide the foundation for learning and producing new content. Ahuman-in-the-loop is necessary to analyze or process the data for models. High-quality and sufficient data is crucial for building an effective generative AI model.

Characteristics of quality training data in generative AI

1. Accuracy: It is crucial to feed AI algorithms with accurate data to produce correct and reliable outcomes. Errors in data can lead AI systems to learn wrong patterns and produce misguided insights, causing potential harm to organizations and individuals.

2. Consistency: Training data for generative AI models must be clean and consistent, enabling AI systems to easily process and analyze the data. Lack of consistency can result in confusion and misinterpretation, impairing the efficiency of AI models.

3. Completeness: Data used to build models should be complete for a given purpose. An incomplete dataset could affect analysis, leading to incomplete or biased results.

4. Relevance: Relevance is one of the important components you should look at when collecting information. With relevant datasets, models can focus on the most important variables. Irrelevant data can clutter the system and produce irrelevant results.

5. Timeliness: Some data tend to become outdated quickly, especially in fast-moving industries. How up-to-date or fresh the data is plays a vital role in the performance of AI systems. Models trained on old data can produce content that is irrelevant or no longer holds, which can have negative consequences.

Consequences of poor quality data

Low-quality data can lead to inaccurate and inconsistent model behaviour. For example, if the AI models are trained on inaccurate, inconsistent, or biased data, the model will produce incorrect and unfavourable outcomes. There are several serious consequences of subpar data.

1. Bias in generative AI: Lack of control over the sources of data used to train generative AI models presents a formidable challenge to audit the training data to handle potential bias. AI models fed with biases propagate discrimination and inequality.

A research piece titledBias in Generative AI published by Carnegie Mellon University revealed that around 8,000 occupational portraits produced by three generative AI tools - Stable Diffusion, Mid-journey, and DALL-E 2 - displayed pervasive gender and racial biases.

2. Inaccurate predictions: Inadequate or erroneous data ingested by AI models can lead to inaccurate predictions. Inaccurate predictions in sensitive areas like healthcare, finance, judiciary, etc., may have dire consequences, impairing patient care, financial stability, and even the safety of individuals.

3. Ethical implications: Poor-quality training data has several far-reaching ethical implications that businesses dabbling in generative AI must be aware of. Some ethical concerns posed by large-scale generative models exposed to low-quality data include misinformation, sensitive information disclosure, data privacy violations, harmful content, plagiarism, and copyright infringement and litigation.

4. Model instability: AI generators trained on inconsistent or contradictory data can make unpredictable decisions. A certain level of inconsistency or unpredictability is incompatible with the reliability needed for real-world situations, rendering these models unfit for deployment.

How to source quality training data

You can source training datasets depending on the use cases and the specific tasks your generative AI model is intended to perform. For example, you would require a conversational dataset to build a large language model (LLM) for customer support chatbot and multimedia databases to generate images, audio, videos, etc.

Here are a few effective ways of sourcing training data for your generative AI solutions.

1. Marketplaces: You can use curated datasets that are cleaned and relevant to your project. Applications fed with large and high-quality data perform efficiently and produce meaningful content. You can buy training datasets from a data marketplace that specializes in curating data tailored to your specific models.

2. Scraping web data: You can also scrape data from various public online sources like websites and social media platforms. This method is suitable if your project needs data from multiple sources for variations in inputs. However, it is advisable to adhere to ethical guidelines and when extracting data from online sources.

3. Data labeling: Data labeling refers to the process of identifying and attaching meaning to data samples to make them suitable for AI training. The process is time-consuming and is done by a human-in-loop collaborator or automated machine. You can outsource data labeling to trusted professionals to ensure data is labeled with utmost precision.

4. Data augmentation: In case you are not able to collect data that meets your requirements, you can re-purpose the existing data to expand the dataset. Augmentation is quite common in computer vision applications. For example, you can rotate and change the color and brightness of images to increase the training data size.

5. Own data: The above options may not work if your project needs domain-specific or proprietary information. In this case, you can leverage your own data to train the AI model. You can tap into information generated across various sources, like reports, policies, online meetings and chats, discussion boards, etc.

Final words

Data plays a critical role in building generative AI models because it directly impacts the efficiency and reliability of the models to generate new content. While high-quality, ethically sourced data enables an application to produce desired and reliable outcomes, reinforcing an element of trust and confidence among users, poor data quality can build unreliable and biased AI applications, which can have serious repercussions in domains like healthcare, finance, criminal justice, etc.

The efficiency of generative AI models depends on collecting and using data responsibly and ethically. To address challenges related to data like data quality, privacy, security, and bias, you need to have a holistic approach that fosters data quality, quantity, and diversity.