Natural language processing advancements such as GPT-3 and BERT have led to innovative model architectures. It is because of pre-trained models that machine learning has become accessible to the general public, allowing even people without a technical background to get their hands on building ML applications without needing to train their models. Most new NLP models are typically trained on a broad range of data, in billions, because they can make accurate predictions, transfer learning, and feature extraction. Unless one is interested in investing much time and energy in building one from scratch, pre-trained models defeat the purpose of training one from scratch. Rather, language models like BERT can be easily fine-tuned and used for various tasks. With the advent of more advanced versions, such as GPT-3, users have had their work made even easier, where they just have to describe what they need, and with a click, can customize the application. They demonstrate their cutting-edge capabilities through such advancements. It can be difficult for many people to comprehend how these pre-trained NLP models compare – case in point: GPT-3 and BERT. Not only do they share many similarities, but the more recent models also tend to exceed the older models in some respect. This article will provide an overview and comparison of each model. BERT BERT, or Bidirectional Encoder Representations from Transformers, is a pre-trained NLP model developed in 2018 by Google. Before the GPT-3 stealing the thunder, BERT was considered the most interesting deep learning NLP model. Using transformer-based architecture, it was able to train a model with the ability to perform at a SOTA level with 2,500 million internet words and 800 million words from the Book Corpus. BERT’s capabilities were demonstrated on 11 NLP tasks, including the Stanford competitive QA dataset. Key Characteristics & Achievements: It is bidirectional. BERT enables users to train their question answering models in just a few hours on a single Cloud TPU and about 30 minutes on a single GPU. Features applications such as Google Docs, Gmail Smart Compose, etc. Scored 80.4% on the General Language Understanding Evaluation (GLUE) and 93.3% on the Squad dataset. Applications: Enhance the customer experience with voice assistance Based on customer reviews Enhance the search for required information based on customer reviews GPT-3 After its major setback with GPT-2, OpenAI created one of the most controversial pre-trained NLP models – GPT-3. GPT-3 is also a large-scale transformer-based language model, similar to BERT, with 175 billion parameters and 10x more than previous models. For tasks such as translations, Q&A, and word unscrambling, the company has displayed its extraordinary abilities. A third-generation language prediction model, which is autoregressive by nature, predicts the outputs based on the input vector words. By combining unsupervised machine learning and few-shot learning, this model works in context. Key Characteristics & Achievements: It is autoregressive. GPT-3 demonstrates how a language model trained on a mass of data can solve a variety of NLP tasks without fine-tuning. It can generate news articles and code. The zero-shot learning benchmark yielded an 81.5 F1; the one-shot benchmark yielded an 84.0 F1, and the few-shot benchmark yielded an 85.0 F1. Achieved 76.2% accuracy on LAMBADA, with zero-shot learning, and 64.3% accuracy on TriviaAQ. Applications: Web and application development To generate machine learning code Creating podcasts and articles In support of legal documents and resumes BERT and GPT models were used for the following tasks: Natural language inference is a task accomplished using NLP that allows models to determine whether a statement is true, false, or undetermined based on a premise. As an example, the premise “tomatoes are sweet” and the statement “tomatoes are fruit” might be classified as undetermined. Question answering allows developers and organizations to create and code question answering systems based on neural networks. Models used for question-answering tasks receive questions about text content and return answers as text, specifically marking the beginning and end of each answer. Text classification is used for sentiment analysis, spam filtering, and news categorization. BERT can be used across any text-classification application to fine-tune content categorization. GPT-3 Vs BERT for NLP Tasks The Right Comparison Between BERT and GPT-3 The GPT-3 and BERT models were relatively new to the industry, but their state-of-the-art performance made them the winners among other natural language processing models. As a result of being trained on 175 billion parameters, GPT-3 becomes 470 times larger than BERT-Large. Furthermore, while BERT requires an elaborate fine-tuning process where users gather examples of examples to train the model for specific downstream tasks, GPT-3’s API allows the user to reprogram it using instructions and access it at any time. As an example, users of BERT will have to train the model on a separate layer of sentence encodings for sentiment analysis and question answering tasks. However, GPT-3 uses a few-shot learning process to determine the output result based on the input token. GPT-3 performs beautifully for general NLP tasks such as translating words, answering questions, performing complex arithmetic and learning new words. In the same way, GPT-3 generates text from a few prompts to produce relevant outputs quickly with a 52% accuracy rate. OpenAI created a mighty monster by simply increasing the size of the model and its training parameters. While, BERT is trained on mask language model tasks, in which 15% of the words in each sequence are randomly masked, to understand the context of the word. Similar to BERT, for sentence prediction, a pair of sentences is fed as input, followed by auxiliary training for prediction. In this case, it processes both sentences to generate a binary label for the sentence prediction. GPT-3’s training approach is relatively straightforward compared to BERT’s in terms of architecture while BERT is trained on latent relationship challenges between texts from different contexts. As a result, GPT-3 is well suited to tasks where sufficient data isn’t available, with a wider range of applications. In contrast to the BERT model, which uses only encoding processes to generate language models, the GPT-3 model employs both encoding and decoding processes to generate a text decoder. A commercially available API makes GPT-3 available, but it is closed-source. BERT, however, has been open-source since its inception, allowing users to customize it based on their needs. While GPT3 generates output one token at a time, BERT, on the other hand, does not use autoregressive models, so it can use deep bidirectional context for predicting outcomes on sentiment analysis and question answering.
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