Natural language processing (NLP) has come a long way over the past few years. With the development of powerful new models such as GPT-3 and BERT, it's now possible to create sophisticated applications that can understand and interact with human language.
However, what went viral as a disruptive chatbot with ChatGPT, suddenly became a contest of language models to power AI content. So, we decided to oppose GPT-3 vs. BERT to understand their differences and similarities, explore their capabilities, and discuss some of the tools that use them.
What is GPT-3?
GPT-3 (Generative Pre-trained Transformer 3) is an autoregressive language model developed by OpenAI. It was trained on a dataset of 45TB of text data from sources such as Wikipedia, books, and webpages. The model is capable of generating human-like text when given a prompt. It can also be used for tasks such as question answering, summarization, translation, and more.
Examples of AI-writing tools based on GPT-3
Several AI content writing tools currently use GPT-3, such as:
What is BERT?
BERT (Bidirectional Encoder Representations from Transformers) is another popular language model developed by Google AI. Unlike GPT-3, BERT is a bidirectional transformer model, which considers both left and right context when making predictions. This makes it better suited for sentiment analysis or natural language understanding (NLU) tasks.
BERT use cases
BERT serves as the base for a number of services, like:
- Google Search Engine
- Huggingface Transformer Library
- Microsoft Azure Cognitive Services
- Google Natural Language API
Differences between GPT-3 and BERT
The most obvious difference between GPT-3 and BERT is their architecture. As mentioned above, GPT-3 is an autoregressive model, while BERT is bidirectional. While GPT-3 only considers the left context when making predictions, BERT takes into account both left and right context. This makes BERT better suited for tasks such as sentiment analysis or NLU, where understanding the full context of a sentence or phrase is essential.
Another difference between the two models lies in their training datasets. While both models were trained on large datasets of text data from sources like Wikipedia and books, GPT-3 was trained on 45TB of data, while BERT was trained on 3TB of data. So, GPT-3 has access to more information than BERT, which could give it an edge in specific tasks such as summarization or translation, where access to more data can be beneficial.
Finally, there are differences in terms of size as well. While both models are very large (GPT-3 has 1.5 billion parameters while BERT has 340 million parameters), GPT-3 is significantly larger than its predecessor due to its much more extensive training dataset size (470 times bigger than the one used to train BERT).
Similarities between GPT-3 and BERT
Despite their differences in architecture and training datasets size, there are also some similarities between GPT-3 and BERT:
- They use the Transformer architecture to learn context from textual-based datasets using attention mechanisms.
- They are unsupervised learning models (they don’t require labeled data for training).
- They can perform various NLP tasks such as question answering, summarization, or translation with varying degrees of accuracy, depending on the task.
GPT-3 vs. BERT: capabilities comparison
Both GPT-3 and BERT have been shown to perform well on various NLP tasks, including question answering, summarization, or translation, with varying degrees of accuracy depending on the task at hand.
However, due to its larger training dataset size, GPT-3 tends to outperform its predecessor in certain tasks, such as summarization or translation, where having access to more data can be beneficial.
On other tasks, such as sentiment analysis or NLU, BERT tends to do better due to its bidirectional nature, which allows it to take into account both left and right context when making predictions. In contrast, GPT -3 only considers left context when predicting words or phrases in a sentence.
The bottom line is that GPT-3 and BERT have proven themselves valuable tools for performing various NLP tasks with varying degrees of accuracy. However, due to their differences in architecture and training dataset size, each model is better suited for certain tasks than others.
For example, GPT-3 is better suited for summarization or translation, while BERT is more beneficial for sentiment analysis or NLU. Ultimately, the choice between the two models will depend on your specific needs and which task you are looking to accomplish.