From BERT to GPT: The Transformation of NLP in the AI Landscape

The field of Artificial Intelligence (AI) is ever-evolving, with Natural Language Processing (NLP) marking significant strides in this progression. This advancement has been largely catalyzed by the advent of Large Language Models (LLMs). BERT and GPT, in particular, have been instrumental in this journey, reshaping the potential and capabilities of AI and NLP. This blog endeavors to navigate through the transition from BERT to GPT, highlighting their distinctive features, wide-ranging applications, and their substantial influence on the AI landscape. We welcome you to delve into the transformation of NLP, viewed through the prism of these pioneering models.

The Pre-LLM Era

In the early days of AI, machines were taught to mimic basic human tasks. The first whispers of understanding human language emerged in what we now call Natural Language Processing (NLP). It was a modest beginning, a foundation on which something greater could be built.

Birth of LLMs

The journey of LLMs began in earnest in the late ’80s and ’90s, with companies like IBM leading the development of smaller language models. The idea of LLMs was first floated with the creation of Eliza in the 1960s: it was the world’s first chatbot, designed by MIT researcher Joseph Weizenbaum.

BERT

BERT, short for Bidirectional Encoder Representations from Transformers, is a Machine Learning (ML) model for natural language processing. It was developed in 2018 by researchers at Google AI Language.

BERT’s Unique Features

BERT’s uniqueness lies in its bidirectional nature. Traditional language models process text sequentially, either from left to right or right to left. This method limits the model’s awareness to the immediate context preceding the target word. BERT uses a bi-directional approach considering both the left and right context of words in a sentence.

BERT uses a transformer architecture, which allows it to handle long-range dependencies in text. It uses self-attention mechanisms, enabling it to focus on different parts of the input when producing an output.

Use Cases and Implementations of BERT

BERT has found applications in a variety of NLP tasks. Here are some of the tests where BERT excels:

1. Sentiment Analysis: BERT can determine how positive or negative a movie’s reviews are. For instance, a movie review platform could use BERT to automatically categorize user reviews as positive, negative, or neutral based on the text. This can help potential viewers to quickly understand the general sentiment towards the movie.
2. Question Answering: BERT helps chatbots answer questions. For example, a customer service chatbot could use BERT to understand and respond to customer queries accurately. This can significantly improve the customer service experience by providing instant responses to customer queries.
3. Text Prediction: BERT predicts your text when writing an email. For instance, Gmail uses BERT for its Smart Compose feature, which suggests completions to your sentences as you type. This can help users write emails more quickly and efficiently.
4. Text Generation: BERT can generate coherent and contextually relevant text. For example, a news organization could use BERT to generate articles about specific topics based on a few sentence inputs. This can help journalists by providing them with a starting point for their articles.
5. Summarization: BERT can quickly summarize long legal contracts. For instance, a law firm could use BERT to generate summaries of long legal documents, helping lawyers to quickly understand the key points without having to read the entire document.
6. Polysemy Resolution: BERT can differentiate words that have multiple meanings (like ‘bank’) based on the surrounding text. For example, a search engine could use BERT to understand the context of a search query and provide more relevant results.
7. Search Query Understanding: BERT helps Google better surface (English) results for nearly all searches since November of 2020. For instance, before BERT, a search for “Can I pick up a prescription for someone” would surface information about getting a prescription filled. But after BERT, Google understands that “for someone” relates to picking up a prescription for someone else and the search results now help to answer that.

The Rise of GPT and Its Impact on BERT

The advent of Generative Pre-trained Transformers (GPT) marked a significant milestone in the evolution of Large Language Models (LLMs). Developed by OpenAI, GPT introduced a novel approach to training LLMs. This approach consists of two stages: unsupervised learning on unlabeled data (pre-training) and fine-tuning the model’s parameters to excel at specific target tasks. This method set a new standard for large language modeling.

GPT’s architecture is decoder-only, with 12 masked attention heads each connected to a feedforward network. This resulted in a model with 117 million parameters. In the pre-training stage, GPT was trained to optimize the chance of a token appearing in the presence of another ordered token in a context window. This training was done on two corpora: The Toronto Book Corpus and WordBenchmark.

GPT’s unique approach to training and its text generational capabilities made it suitable for LLMs. Most of the LLMs we see today are upgraded versions of GPT. While BERT significantly advanced the state of the art in natural language understanding tasks, GPT showcased a powerful model for understanding and generating human-like text.

GPT’s impact on BERT is significant. While BERT has already been pre-trained and requires very little data for an array of NLP tasks, GPT introduced a new standard in training LLMs. Even today, in LLMs like GPT and RAG models, we see BERT being used. This is because the distinguishing features of BERT, such as its bidirectional nature and transformer architecture, are still valid and beneficial for LLMs.

GPT-2: A Leap in Text Generation

Released by OpenAI in 2019, GPT-2 was a significant leap in the development of LLMs. With 1.5 billion parameters, GPT-2 demonstrated an unprecedented ability to generate coherent and contextually relevant text. Its key advancements included:

• Scalability: GPT-2 was trained on a diverse dataset called WebText, which included 8 million web pages. This extensive training enabled the model to handle a wide range of topics and contexts.
• Zero-shot Learning: GPT-2 could perform tasks it wasn’t explicitly trained for, simply based on the instructions provided in the input text. This ability to generalize across tasks without specific training was groundbreaking.

GPT-3: Scaling Up

GPT-3, launched in June 2020, built on the success of GPT-2 with an astonishing 175 billion parameters, making it the largest model of its time. Key features of GPT-3 included:

• Few-shot Learning: GPT-3 could perform tasks with minimal examples, thanks to its massive size and diverse training data.
• Versatility: GPT-3 excelled in a variety of tasks, from language translation and question-answering to generating creative content like poetry and code.
• API Access: OpenAI provided API access to GPT-3, allowing developers to integrate its capabilities into various applications, significantly broadening its impact.

GPT-4: The Latest Advancement

GPT-4, introduced in 2023, continued the trajectory of increasing capabilities and applications. While specific parameter details have not been disclosed, GPT-4 emphasized:

• Enhanced Performance: Improved natural language understanding and generation, leading to more accurate and nuanced responses.
• Multimodal Capabilities: GPT-4 incorporated multimodal inputs, including text and images, enabling it to understand and generate content that involves both modalities.
• Ethical Considerations: Greater focus on ethical use, bias reduction, and transparency, addressing some of the concerns raised with earlier models.

The Future of GPT Models

The future of GPT and similar LLMs is promising, with several trends and innovations on the horizon:

• Continued Scaling: Future models will likely continue to scale up, incorporating even more parameters and training data to improve performance across diverse tasks.
• Specialization: There will be an emphasis on creating specialized models for specific industries and applications, such as healthcare, finance, and legal services.
• Integration with Other AI Technologies: Future models will integrate more seamlessly with other AI technologies like computer vision and speech recognition, enabling more comprehensive and versatile AI systems.
• Ethical AI: As LLMs become more integrated into society, there will be a greater focus on ensuring ethical AI practices, including fairness, transparency, and accountability.
• Human-AI Collaboration: The role of LLMs will evolve towards augmenting human capabilities, providing tools that enhance productivity, creativity, and decision-making

The journey from the inception of NLP to the development of LLMs like BERT has been a fascinating one and journey from GPT-2 to GPT-4 has been marked by significant advancements in scalability, performance, and versatility. As we look to the future, we can expect LLMs to continue pushing the boundaries of what is possible, driving innovation across various fields and contributing to the broader goal of creating more intelligent and ethical AI systems.