BERT vs. GPT: What’s the Difference?

ChatGPT is one of the hottest topics in artificial intelligence because of its efficacy and adaptability in completing business functions or content development tasks. However, GPT—generative pre-train transformers—isn’t the only leading language processing model.

Bidirectional encoder representations from transformers (BERT) is a similar technology developed by Google. Both technologies represent significant strides in artificial intelligence and have several similarities.

Both of these applications use transformer architecture. Transformer architecture is a framework of machine learning that is particularly efficient when it comes to natural language processing (NLP). Google developed transformers in 2017, representing a breakthrough that resulted in NLP systems becoming more capable of identifying language and accurately placing it into a sequence.

If you're interested in this technology, read on to discover their similarities and differences, details of their development, and applications to real-world tasks and use cases.

Read more: ChatGPT-3.5 vs. 4: What’s the Difference?

GPT

Parent company OpenAI developed ChatGPT for release in late 2022 after testing several iterations of the model in previous years. Precursors to ChatGPT included InstructGPT, GPT, GPT-2, and GPT-3. ChatGPT—the first version released to the public—evolved from InstructGPT to gain the capability to admit mistakes, answer follow-up questions, and reject inappropriate content.

The popularity of ChatGPT exploded since its release to the public. OpenAI set a record in February 2023 by becoming the fastest-growing app to ever hit 100 million users, reaching the mark in about two months [1]. Microsoft invested $1 billion in OpenAI in 2019 and increased its investment in 2023 to a multibillion-dollar, multiyear partnership [2, 3].

What is GPT used for?

GPT, whose technology incorporates several aspects of artificial intelligence, including machine learning and neural networks, functions as a conversational information resource. ChatGPT generates responses to questions or prompts in the form of natural language, which humans can easily understand.

The ability to produce text at a rate far faster than a human can synthesize information, summarize large amounts of text, and respond to various prompts means learners can use ChatGPT to assist with understanding academic assignments. Among teens who know of ChatGPT, 19 percent say they use it for schoolwork, according to the Pew Research Center [4].

Read more: 8 Common Types of Neural Networks

Advantages of ChatGPT

Compared to BERT, ChatGPT is better when addressing a specific use case, such as potentially improving customer experience, aiding students and instructors in academia, or helping create more efficient business operations. This is because, in part, the technology can produce an easily understood response to a prompt, and it learns more as it receives more data. Conversely, BERT is an alternative best used when seeking more context to explain or analyze data.

ChatGPT trains on a great deal of information, making it particularly nimble when responding to any prompt. Marketing is a common use case for ChatGPT because the technology can generate keywords, provide an outline for blog posts, and translate copy in response to specific instructions given by the user.

Disadvantages of GPT

GPT is unidirectional, which means it processes language only from right to left. This puts GPT at a disadvantage to BERT, which understands language bidirectionally for enhanced context.

ChatGPT’s capability to process and generate language has come a long way since its early development. However, efficacy, safety, and security issues limit ChatGPT’s current capabilities.

The proliferation of technology such as ChatGPT raises many political or ethical questions about its use. In October 2023, the White House issued an executive order that laid out eight guiding principles to ensure AI's safe, ethical use. Both private industry and government agencies are to adhere to these principles.

Safety involving content and information continues to be a concern for ChatGPT. The platform has been reported to produce inappropriate content in response to some prompts. Unauthorized storage or access to sensitive business information persists as a concern, too, with a Fishbowl survey finding that 70 percent of respondents who used AI at work did not tell their boss about it [5].

BERT

Google developed BERT in 2018, a momentous year for artificial intelligence in which BERT and OpenAI’s GPT debuted. BERT is a neural network trained on a vast plain text corpus— a collection of written texts that computers can understand.

BERT uses a bidirectional method of analyzing language, which allows it to understand the context around words and sentences. The system is particularly adept at semantic analysis and can understand which sentences should come before or after others.

Read more: What Is the BERT Model and How Does It Work?

What is BERT used for?

BERT creates outputs for many use cases, including sentence generation, sentiment analysis, or identification of people or places in a piece of text. BERT has many use cases, ranging from language generation to sentiment analysis to search engine optimization. Here are some common applications of BERT.

1. Online searches

BERT can predict human language, which makes it useful for fulfilling search queries. BERT’s ability to predict related questions and find language that relates closely to an input in a Google search allows it to sufficiently provide search results. Pandu Nayak, VP of Search at Google, wrote soon after BERT’s development that it helped Google enhance one in 10 searches in the English language [6].

2. Sentiment analysis

BERT can analyze a large number of articles or op-eds and classify them based on opinion or attitude. It has been shown to be accurate in detecting sentiment and classifying language based on the sentiment expressed.

3. Named entity recognition

Another key function of BERT is identifying names of people, places, or things. BERT can analyze language and identify key information. Named entity recognition is useful for classifying information by topic, prominent person or place, or anything else items may have in common. Furthermore, Multilingual BERT (M-BERT) is a version of BERT trained in a vast volume of 104 languages, which means it can analyze languages other than English well. 

Advantages of BERT

Though ChatGPT and BERT use the transformer architecture, they differ in how they specifically process and generate language. BERT uses bidirectional context representation, which processes text from right to left and left to right. This allows BERT an increased capability to generate language based on context. In comparison, ChatGPT generates language word by word based on the previous word it generated. BERT captures context from words that came before and those that will come after.

BERT trains on pieces of text by masking words in the input and choosing the correct word that would fit in the space. For example, in the sentence, “The boy played basketball in the park this afternoon,” the bidirectional model would mask words such as “basketball” or “park” to then predict and learn where they would make sense in the sentence.

Bidirectionality is not a new concept, but Google made a big breakthrough in machine learning when it developed BERT, the first deep neural network capable of pre-training using bidirectionality.

BERT is available open-source by Google, allowing users to train the network on new data. The system is already trained on a large breadth of data, so users can plug in their own much smaller sets of data to use BERT more quickly. Training a neural network from scratch takes an extremely large data set, which requires a great deal of time and resources.

Disadvantages of BERT

While anyone with an OpenAI account can access ChatGPT, putting BERT to use is a little more complicated. You can access Google’s open-source code for BERT using a Jupyter Notebook. Programmers, data analysts, and scientists use this tool to access source code to use in Python or R.

BERT is a large system with many weights—a term referring to the signals between neurons, which are learned by the neural network through training–that must be adjusted to ensure proper operation. This can be a downside compared to ChatGPT, which can produce long, detailed outputs even with inputs of only a few sentences.

Getting started with Coursera

You can learn more about artificial intelligence applications such as BERT and ChatGPT with the Prompt Engineering Specialization offered by Vanderbilt University on Coursera. This course aims to help you understand how to get the most out of ChatGPT by writing prompts that generate desirable outputs. You can amplify human creativity with the help of ChatGPT by learning how to optimize inputs in pursuit of well-written, efficient AI-generated outputs.