What is GPT?
GPT is a generative AI technology that has been previously trained to transform its input into a different type of output.
Generative: Generative AI is a technology capable of producing content, such as text and imagery.
Pre-trained: Pre-trained models are saved networks that have already been taught to resolve a problem or accomplish a specific task using a large data set.
Transformer: A transformer is a deep learning architecture that transforms an input into another type of output.
Chat-GPT, an artificial intelligence (AI) chatbot app based on the GPT 3.5 model that mimics natural conversation to answer questions and respond to prompts, is one of the most well-known use cases for GPT.2018 saw the development of GPT by OpenAI, an AI research lab. Since then, OpenAI has officially released three iterations of the GPT model: GPT-2, GPT-3, and GPT-4.
What is GPT 3?
Generative Pre-trained Transformer 3 (GPT-3), introduced by OpenAI in 2020, represents a significant leap in language modeling technology. Much like its precursor, GPT-2, it operates as a decoder-only transformer model within a deep neural network framework, surpassing traditional recurrence and convolution-based architectures through the implementation of attention mechanisms. This unique attention mechanism empowers the model to selectively concentrate on pertinent segments of input text, enhancing its predictive capabilities. With a context length of 2048 tokens and utilizing float16 (16-bit) precision, GPT-3 boasts an unprecedented scale, featuring a staggering 175 billion parameters. This expansive architecture demands substantial storage resources, requiring approximately 350GB of space due to each parameter occupying 2 bytes. Notably, GPT-3 has demonstrated remarkable prowess in "zero-shot" and "few-shot" learning scenarios across various tasks, underscoring its versatility and effectiveness in natural language processing.
Examples of GPT models
Content Summarization and Paraphrasing
Code Generation and Automation
Content Generation
Chatbots and Virtual Assistants
Language Translation
What is BERT?
BERT, which stands for Bidirectional Encoder Representations from Transformers, is a powerful natural language processing model introduced by Google in 2018. Unlike traditional models that process words in a left-to-right or right-to-left manner, BERT is designed to understand the context of words in a bidirectional manner. This bidirectionality allows BERT to capture the meaning of words based on their surrounding context, leading to more accurate language understanding.
BERT is based on the Transformer architecture, a type of deep learning model that has shown significant success in various NLP tasks. It consists of multiple layers of self-attention mechanisms and feedforward neural networks, enabling it to learn complex patterns in text data.
One of the key innovations of BERT is pre-training on large amounts of text data using two unsupervised learning tasks: masked language modeling (MLM) and next sentence prediction (NSP). During MLM, BERT randomly masks some of the words in a sentence and then predicts the masked words based on the context. This encourages the model to understand the relationships between words within a sentence. During NSP, BERT learns to predict whether two sentences in a document are consecutive or not, which helps it understand the relationships between sentences.
Examples of BERT
BERT is used for a wide variety of language tasks. Below are examples of what the framework can help you do:
Determine if a movie’s reviews are positive or negative
Help chatbots answer questions
Help predicts text when writing an email
Can quickly summarize long legal contracts
Differentiate words that have multiple meanings based on the surrounding text
Differences between GPT-3 and BERT
Main goal
ChatGPT-3 generates text based on the context and is designed for conversational AI and chatbot applications. In contrast, BERT is primarily designed for tasks that require understanding of the meaning and context of words. So, it is used for such NLP tasks as sentiment analysis and question answering.
Architecture
Both language models use a transformer architecture that consists of multiple layers. GPT-3 has an autoregressive transformer decoder. It means the model generates text sequentially from left to right and in one direction, predicting the next word based on the previous one.
BERT, on the contrary, has a transformer encoder and is designed for bidirectional context representation. It means that it processes text both left-to-right and right-to-left, thus capturing context in both directions.
Model size
GPT-3 is made up of 175 billion parameters, while BERT has 340 million parameters. It means GPT-3 is significantly larger than its competitor due to its much more extensive training dataset size.
Fine-tuning
GPT-3 is typically fine-tuned on specific tasks during training with task-specific examples. It can be fine-tuned for various tasks by using small datasets.
BERT is pre-trained on a large dataset and then fine-tuned on specific tasks. It requires training datasets tailored to particular tasks for effective performance.
GPT-3 vs. BERT: capabilities comparison
Category | GPT-3 | BERT |
Model | Autoregressive | Discriminative |
Objective | Generates human-like text | Recognizes sentiment |
Architecture | Unidirectional: it processes text in one direction using a decoder | Bidirectional: it processes text in both directions using an encoder |
Size | 175 billion parameters | 340 million parameters |
Training data | It is trained on language modeling by using hundreds of billions of words | It is trained on masked language modeling and next sentence prediction by using 3.3 billion words |
Pre-training | Unsupervised pre-training on a large data | Unsupervised pre-training on a large corpus of text |
Fine-turning | Does not require but can be fine-tuned for specific tasks | Requires fine-tuning for specific tasks |
Uses cases | Coding | Sentiment analysis |
| ML code generation | Text classification |
| Chatbots and virtual assistants | Question answering |
| Creative storytelling | Machine translation |
| Language translation |
|
Accuracy | Based on the SuperGLUE benchmark, 86.9% | Based on the GLUE benchmark, 80.5% |
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