Comparing GPT and BERT

The generative pre-trained transformers (GPT) are a family of large language models based on artificial neural networks that are pre-trained on large datasets of unlabelled text and able to generate novel human-like text developed by Google researchers and were introduced in 2018 by OpenAI. GPT-3 is the latest and most advanced GPT model with 175 billion parameters and was trained on 400 billion text tokens.

BERT is another language model developed by Google that is pre-trained on large amounts of data. BERT stands for Bidirectional Encoder Representations from Transformers. BERT uses both left and proper contexts to create word representations. It is a multi-layer bidirectional Transformer encoder. While evaluating benchmark datasets, BERT has achieved state-of-the-art results in several natural language processing (NLP) tasks. In terms of performance and architecture differences between GPT and BERT, GPT models typically perform well when generating long-form text, such as articles or stories.

While both are pre-trained on large text datasets, their training methods, tasks handled, and performance metrics differ. Understanding these differences is crucial to determining which model most applies to a particular NLP task.

In contrast, the BERT model is better suited for NLP tasks that require language understanding, such as question-answering or sentiment analysis. Overall, both GPT and BERT are powerful NLP models that have been shown to excel in different areas of natural language processing.

GPT models can generate natural language text that can be used as a search query for internet searches. For instance, given a prompt such as "Search for the best restaurants in New York City."

BERT could be utilized to understand the intent of the user's search query and provide more accurate results. For instance, if a user types in a search query like "What is the capital of France?", BERT can infer the question being asked and provide the relevant answer, "Paris." (See AI HIVE).