Large language models (LLMs) are a type of artificial intelligence (AI) that are trained on massive amounts of text data, including books, articles, code, and various other forms of text. LLMs can then be employed for a wide range of tasks, such as generating text, translating languages, and answering questions.
The progress of LLMs has been nothing short of remarkable in recent years. Starting with the introduction of the BERT model in 2018, which significantly improved upon previous LLMs, the field of natural language processing (NLP) has witnessed rapid advancements that have led to more powerful and capable AI models.
A Timeline of Major LLM Breakthroughs
The advancements in large language models over the past few years have been nothing short of extraordinary. In this section, we will delve deeper into each of the major breakthroughs to better appreciate their contributions to the field of natural language processing.
Bidirectional Encoder Representations from Transformers (BERT) was a groundbreaking model introduced by Google AI in 2018. BERT utilized the Transformer architecture, which allowed it to process input data in parallel rather than sequentially. This parallel processing gave BERT the ability to learn complex patterns in language and understand the context of words in a sentence.
BERT's bidirectional training approach was a major innovation, as it allowed the model to learn both the context before and after a given word, leading to a more accurate understanding of the text. BERT quickly became a popular choice for a wide range of NLP tasks, including sentiment analysis, question answering, and named entity recognition.
In 2019, OpenAI released the second iteration of the Generative Pre-trained Transformer (GPT-2). GPT-2 was a significant leap forward, with a substantially larger model size and a more powerful architecture than BERT. GPT-2 could tackle many NLP tasks that were previously considered difficult or impossible, including text summarization, machine translation, and text completion.
One of the most impressive aspects of GPT-2 was its ability to generate human-like text, which sometimes made it difficult to distinguish between content generated by the model and content written by a human. This capability raised concerns about the potential misuse of the technology, leading OpenAI to initially withhold the release of the full model.
In 2020, Facebook AI introduced a new type of large language model called Bidirectional and Auto-Regressive Transformers (BART). BART combined the best aspects of BERT and GPT-2, benefiting from both bidirectional and auto-regressive learning. Trained on a dataset of text and code, BART excelled at both NLP and programming tasks.
BART's hybrid approach allowed it to perform tasks such as question answering, summarization, and translation with improved accuracy. Its ability to understand and generate code made it particularly popular in both research and industry settings.
2021-22: GPT-3 and ChatGPT
In 2021, OpenAI released the third iteration of the Generative Pre-trained Transformer (GPT-3), which was even more powerful than GPT-2. With 175 billion parameters, GPT-3 was capable of performing tasks that were previously thought impossible for AI models, such as composing poetry, writing code, and even designing simple web pages.
GPT-3's human-like text generation capabilities were further refined, making it an invaluable tool for various applications, such as content generation, programming assistance, and more. Despite the ethical concerns surrounding its potential misuse, the launch of ChatGPT (initially powered by GPT 3.5) demonstrated the vast potential of large language models in transforming human-computer interaction.
In 2023 and beyond, the progress of LLMs has continued with the introduction of even larger and more powerful models than GPT-3. Capable of increasingly complex tasks such as creating diverse forms of creative content, translating languages, and answering questions informatively, these models have the potential to revolutionize the way we interact with computers and create applications that are more natural and user-friendly than ever before.
The Rising Importance of Multimodal Models
As large language models continue to advance rapidly in capability and scale, there has been growing interest in developing multimodal models that can understand and generate not just text but also images, audio, and video. These models aim to enable richer, more engaging human-AI experiences by integrating multiple data types.
For example, Anthropic’s Constitutional AI uses natural language feedback to help align model behavior with human values. Their technique prompts people to provide feedback on model-generated text, which is then used to update the model. By incorporating feedback in addition to traditional language data, Constitutional AI aims to make models more helpful, harmless, and honest.
Other companies like OpenAI and DeepMind are exploring how to apply similar alignment techniques to multimodal agents that can perceive and respond using various media. For instance, a virtual assistant might communicate using speech, text, and on-screen visual components together, with its behavior aligned to human values through feedback on any or all of these modalities.
Generative multimodal models can also be used for data augmentation to improve performance on downstream tasks. For example, a model like DALL-E that generates images from text descriptions could be used to produce additional training data for image classification models. The generated images would be labeled implicitly by the text used to create them, reducing the need for manual data annotation.
However, developing and applying multimodal models also introduces challenges around how to evaluate, govern and ensure the responsible development of systems that have a greater range of possible behaviors and effects 1. If models can understand and generate human-like speech, text, images, video and more, their opportunities for impact are far greater, whether positive or negative.
Overall, multimodal models are an active area of research that present exciting new possibilities for building human-AI interfaces and applications as well as new concerns around their advancement. By incorporating multiple data types, these models may achieve new levels of nuance, personalization, and context that could improve assistive technologies, personalized content, creative tools and beyond. However, their added complexity will require new techniques to keep them aligned with human values and priorities. Progress in multimodal models is poised to change the way we build and interact with AI, for better or worse; ensuring this progress benefits and respects humanity may be one of the greatest challenges in the development of advanced AI.