When OpenAI introduced ChatGPT in 2022, it created a near-historical milestone in conversational AI. ChatGPT is one of the most advanced AI chatbots powered by a highly sophisticated language model.
What makes ChatGPT a cut above the rest? Experts point out that the chatbot is powered by an extensive data annotation process that goes into its model training. ChatGPT could accurately interpret human language thanks to vast amounts of human-labeled text data.
Annotations are crucial to a chatbot’s (like ChatGPT) ability to converse intricately and provide insightful responses. The technologies driving the ability to process, understand, and speak any human language are Natural Language Processing (NLP) and Machine Learning (ML).
What is the reason for this sudden explosion in high-end technologies? Let’s explore.
What is Driving the AI Revolution?
Natural Language Processing is a pulsing buzzword in the tech world. The global NLP market is projected to reach an expected value of USD 91 billion by the year 2030. The market is already growing at a steady CAGR of 27% and is expected to grow from $21.17 billion in 2022 to $209.91 billion by 2029, with a CAGR of 38.8%.
The existing Large Language Models or LLMs are all powered by NLP and ML that are, in turn, trained with very high-quality training data. This is what determines the success of these AI applications.
What’s training data?
Training data are sets (input and output pairs) of examples on which Machine Learning models are trained to make accurate predictions. The ML models use the input-output pairs to learn how to map inputs to the corresponding outputs. This mapping is the project’s foundation and is the learning basis for all ML models.
This concept is better explained with an example. Take the sentiment analysis task, for instance. The training data for this task comprises a set of reviews and corresponding sentiment labels like:
- Fabulous > positive
- Unacceptable > negative
- Functional >neutral
The model is trained on this kind of data to learn how to predict the sentiment of new reviews. The concept is simple: The higher the sample quality, the more accurate the output. ChatGPT 3 is an ideal example of this concept. The chatbot was trained on 176 billion parameters, 570 GB of books, articles, websites, and other textual data taken from the Internet.
Where is the ChatGPT’s Data Sourced From?
Basically, ChatGPT is fed WebText datasets comprising nearly 8 million web pages taken from the internet, along with additional datasets to enhance its performance.
The WebText datasets which refers to data taken from the Internet, provide easy access to information. The diverse collection covers various sources like online forums, websites, and new articles. The additional datasets comprising text sources like written works, articles, and books make the training data diverse for developing LLMs like ChatGPT.
So, how was ChatGPT trained? Let’s unravel that puzzle.
How ChatGPT Was Trained: A Step-by-Step Guide
Data annotation is the key element used to construct LLM as advanced as ChatGPT. The main process here was adding meaningful tags to text data to enable the AI model to understand the context and meanings behind phrases and words. Using data annotation as the core element, ChatGPT was developed using these steps:
Step 1: Data collection
To build such an advanced Chatbot, OpenAI used a massive corpus of text data from numerous online sources. All irrelevant and duplicate information was then removed from this enormous data collection to clean it up.
Step 2: Data labeling
All the collected data were annotated by a skilled team of annotators who were trained to apply the labels with complete precision. The labels included:
- Pat-of-speech tagging
- Text classification
- Sentiment labels
- Named entity recognition
Step 3: Training the model
By using transformer architecture, the language model was trained using the annotated data. The model was trained to predict the most suitable labels for words or phrases based on the context and annotated data.
Step 4: Evaluation & Fine-tuning
A separate dataset evaluated ChatGPT’s ability to accurately predict labels in new, unseen texts. The evaluation results were then used to fine-tune the AI model until it achieved the desired performance level.
Step 5: Deployment
The trained and fine-tuned ChatGPT was deployed and made available for real-time usage allowing users to generate natural language responses to their inputs.
How Data Annotation Fuelled ChatGPT’s Conversational Capabilities
As a starting point, ChatGPT was trained using transformer-based language modeling.
Basically, ChatGPT’s architecture follows the concept of transformer architecture. It comprises a multi-layer encoder-decoder system and self-attention capabilities. The self-attention capabilities allow it to focus on various input aspects as it generates output.
During the training phase, ChatGPT’s parameters were modified by exposing them to vast volumes of text data. The aim was to minimize the disparity between the model-generated text and the target text.
Identifying patterns in the text data was necessary to create contextually appropriate and semantically sound text. The fully trained model was then deployed for several Natural Language Processing tasks like:
- Finding answers to questions
- Language Translation
- Text creation
ChatGPT is powered by the GPT-3 model, which was trained using annotated data, which provided it with a wealth of information, including named entities, coreference chains, and syntax trees. This data annotation enabled ChatGPT’s model to completely understand text generation and comprehension in multiple genres and styles.
ML and AI applications depend heavily on data labeling to ensure the accuracy and quality of the data used to train effective ML models. Furthermore, the text data was basically annotated manually by a team of annotators trained to label accurately and consistently.
To ensure data accuracy and quality, labelers annotated the data using automated methods in some cases.
How ChatGPT Eases The Work For Data Annotators
ChatGPT is a boon for data annotators. This amazing AI tool helps annotators with the following tasks:
- Classifying sentences into various categories like intent, sentiments, and other topics.
- Identifying named entities in texts, such as locations, dates, organizations, and people.
- Extract structured information from unstructured data like product prices and names.
- Use input prompts to generate texts that can later be used to create examples of text data that can be labeled and annotated.
- Detect and rectify errors and inconsistencies in text data to save time and improve annotation accuracy.
- Summarize large volumes of text data to help annotators understand the data content and context.
At this juncture, it should be noted that although ChatGPT and other advanced chatbots make these tasks possible, the accuracy of the final output is not possible without annotators.
As for ChatGPT, it has created a tizzy in the tech world by making itself an indispensable tool for umpteen applications, from content creation to information retrieval and customer service. ChatGPT’s comprehensive capabilities and response to conversational cues are changing the face of NLP.
Opporture: A US-based Data Annotation Company
So, there you have it- an in-depth analysis of how ChatGPT is sitting on a solid foundation like data annotation that gives it its capabilities. Wish to collaborate with a suitable data annotation company that delivers high-quality data annotation and other services? Opporture can help! Opporture is an AI model training company in North America offering various AI-powered content-related services.
Do call us to discuss how we can help you.
