Language Model Face-Off: Comparative Insights Into GPT and BERT

The popularity of ChatGPT is a testament to how far natural language processing (NLP) has come. Transformer architecture models like GPT-3, GPT-4, and BERT are capable of human-like conversations, and some can even be used to write complex code.

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While GPT is the market leader, BERT was actually the first language model that arrived on the scene in 2018. But which one is better? And what’s the difference between GPT and BERT?

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Explaining GPT-3 and GPT-4

GPT-3 (Generative Pre-trained Transformer 3) is an autoregressive language model launched by OpenAI in June 2020. It utilizes a transformer architecture with 175 billion parameters, making it one of the largest language models ever constructed.

GPT-3 can generate natural language text, as well as answer questions, compose poetry, and even write complete articles. ChatGPT is a prime example of generative AI powered by GPT.

It has been deemed a game-changer for natural language processing, and it has a wide range of potential applications, including chatbots, language translation, and content creation.

GPT-4 is the latest and largest in a series of GPT models, and is accessible if you have a ChatGPT Plus subscription . GPT-4 is six times larger than the GPT-3 model, with an estimated one trillion parameters, making it much more accurate.

What Is BERT?

BERT (Bidirectional Encoder Representations from Transformers) is a pre-training language representation model that fine-tunes NLP applications created by Google in 2018. Unlike other NLP models that use unidirectional attention flow, BERT uses bidirectional flow, which allows it to use context from both directions during processing.

This allows the model to understand the meaning of words in context and, in turn, better comprehend language structures. With BERT, Google can now provide more accurate search results for complex queries—particularly those that rely on prepositions such as “for,” “to,” and “from.”

The Main Differences Between GPT and BERT

Now that you have a brief idea about GPT and BERT, let’s discuss the main differences between these two language models.

Architecture

Architecture refers to the numerous layers that form a machine-learning model. GPT and BERT use different models. BERT is designed for bidirectional context representation, which means it processes text from both left-to-right and right-to-left, allowing it to capture context from both directions.

In contrast, humans read text from left to right (or right to left, depending on your locale). BERT is trained using a masked language modeling objective, where some words in a sentence are masked, and the model is tasked with predicting the missing words based on the surrounding context.

This pre-training method allows BERT to learn deep contextualized representations, making it highly effective for NLP tasks like sentiment analysis, question-answering, and named entity recognition.

In contrast, GPT is an autoregressive model, meaning it generates text sequentially from left to right, predicting the next word in a sentence based on the words that came before it.

GPT is trained using a unidirectional (causal) language modeling objective, where it predicts the next word given the context of previous words. That’s one of the main reasons why GPT is so popular for content generation.

Training Data

BERT and GPT differ in the types of training data they use. BERT is trained using a masked language model, meaning certain words are masked, and the algorithm has to predict what the next word is likely to be. This helps train the model and makes it more contextually accurate.

Like GPT, BERT is trained on a large-scale corpus of text. The original was trained on the English Wikipedia and BooksCorpus, a dataset containing approximately 11,000 unpublished books, which amounts to about 800 million words, from various genres such as fiction, science, and computing.

BERT can be pre-trained on different language models, which, as mentioned above, allows it to be trained for specific applications, with the added option to fine-tune this pre-trained model.

Conversely, GPT-3 was trained on the WebText dataset, a large-scale corpus containing web pages from sources like Wikipedia, books, and articles. It also includes text from Common Crawl, a publicly available archive of web content. And it can also be fine-tuned for specific purposes.

As for GPT-4, training data information is a bit scarce, but it’s quite likely that the GPT-4 is trained on a similarly diverse dataset, potentially including newer sources and an even larger volume of data to improve its understanding of natural language and its ability to generate contextually relevant responses.

Use Cases

While both are highly versatile NLP models, their architectural differences set them apart in a few ways. For instance, BERT is far more capable for the following use cases:

1. Sentiment Analysis: BERT can better understand the overall sentiment of a given text as it analyzes words in either direction.
2. Named Entity Recognition: BERT is capable of recognizing different entities in a specific piece of text, including locations, people, or organizations.
3. Answering Questions: Because of its superior comprehension capabilities, BERT is more capable of extracting information from text and answering questions accurately.

The GPT learning model is no slouch, either. While sentiment analysis might not be its forte, GPT excels in several other applications:

1. Content Creation: If you’ve used ChatGPT, you probably know about this already. When it comes to content creation, GPT outsmarts most other models. Just write a prompt, and it’ll churn out a perfectly coherent (though not always accurate) response.
2. Summarizing Text: Just copy-paste a large block of text in ChatGPT and ask it to summarize it. It’s capable of summarizing text while maintaining the core information.
3. Machine translation: GPT can be fine-tuned for translating text from one language to another, thanks to its ability to generate text based on context.

Usability

Unlike ChatGPT, which lets anyone leverage the GPT model, BERT is not as readily available. First, you’ll have to download the originally published Jupyter Notebook for BERT and then set up a development environment using Google Colab or TensorFlow.

If you don’t want to worry about using a Jupyter Notebook or aren’t as technical, you could consider using ChatGPT, which is as simple as just logging into a website. However, we’ve also covered how to use Jupyter Notebook , which should give you a good starting point.

BERT and GPT Show the Capabilities of AI

BERT and GPT training models are clear examples of what artificial intelligence is capable of. ChatGPT is more popular and has already resulted in several additional applications, such as Auto-GPT, which are disrupting workflows and changing job functions.

While there’s skepticism around AI adoption and what it may mean for jobs, the potential for good is also there. Many companies like Google and OpenAI are already working to establish controls and further regulate AI technology, which could bode well for the future.

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While GPT is the market leader, BERT was actually the first language model that arrived on the scene in 2018. But which one is better? And what’s the difference between GPT and BERT?

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