Navigating the age of transformers

Transformers have quickly become the go-to architecture for natural language processing (NLP). As a result, knowing how to use them is now a business-critical skill in your AI toolbox. Here will go through many of the key large language models (LLMs) developed since OpenAI first released GPT-3, as well as the key contributions of each of these LLMs.

The Transformers model, which was the predecessor to the widely used BERT model, was first introduced by researchers at Google in April 2017. The Transformer architecture was developed to address the limitations of previous sequential neural architectures, particularly with language modeling and machine translation tasks.

The Transformer model proved to be highly effective in parallelizing computations, leading to significant improvements in training times. It also introduced the concept of attention mechanism, which allows the model to selectively focus on relevant parts of input and output sequences.

The Transformer architecture paved the way for many subsequent models, including BERT, GPT-2, and GPT-3, which further advanced natural language processing research.

Transformers were initially trained using datasets with few examples and had some limitations and drawbacks compared to state-of-the-art language models that are available today. Some of these limitations include:

1. Limited training data: During the initial stages, transformers were only trained on small datasets, which severely limited their ability to process and generate human-like language.
2. Out-of-vocabulary words: Transformers struggle to handle out-of-vocabulary (OOV) words. They require a predefined vocabulary of words, and any new words that are not in that vocabulary will not be recognized.
3. Bi-directional processing: Initially, transformers were only able to process sequences of words in one direction, from left to right. This made it hard for the model to understand the entire context of a sentence and often resulted in poor performance on natural language processing tasks.
4. Computationally intensive: Transformers are computationally intensive models, requiring a big amount of computing resources to train and deploy. This makes them less accessible to the industry and academia.
5. Training data required labelling

However, with the advances in hardware, software, and natural language processing research, these drawbacks have been mitigated through larger datasets, better tokenization techniques, bi-directional processing, and more efficient training and inference methods. As a result, modern transformer-based models, such as GPT series, BERT and XLNet, now achieve state-of-the-art performance on many natural language processing tasks.

BERT (Bidirectional Encoder Representations from Transformers)

BERT model was developed after the Transformers model. BERT was first introduced in a research paper by researchers at Google in 2018.

BERT was trained on a large amount of text data in an unsupervised manner using a combination of both MLM (Masked Language Modeling) and Next Sentence Prediction (NSP) pre-training objectives

BERT builds upon the transformer architecture of the original Transformers model, but introduces a new training methodology called MLM (Masked Language Modeling) which involves randomly masking a certain percentage of tokens in an input sequence and then training the model to correctly predict the masked tokens. This approach allows the model to learn bidirectional context representations for each word in the sentence.

The NSP training objective involves training the model to predict whether two sentences are consecutive or not. This task helps the model understand the relationships between different sentences and improves the contextual representation of the model.

Moreover, BERT has the ability to pre-train on massive amounts of text data and can use its learned knowledge to perform well on downstream NLP tasks with relatively little training data. BERT achieved state-of-the-art results on several natural language processing tasks such as sentiment analysis, language modeling, question-answering, and more. Since then, many new transformer-based models like GPT-2, GPT-3, and T5 have been developed based on the same architecture with some modifications.

The training data used to pre-train BERT was primarily sourced from the BooksCorpus and English Wikipedia, comprising of around 3.3 billion words. The text was pre-processed by tokenization, in which each word was converted to its subword units, allowing the model to handle out-of-vocabulary words.

Once the model was pre-trained on the large corpus of data, it was fine-tuned on various specific NLP tasks such as natural language understanding, named entity recognition, and question-answering. The fine-tuning process involved training the model on the specific task using task-specific training data with the original BERT weights frozen, followed by a few epochs of fine-tuning with a low learning rate to update the specific task's weights.

BERT is a highly versatile natural language processing (NLP) model that can be used for various tasks. It is used across a range of industries, including finance, healthcare, and e-commerce, to name just a few. Here are some of its common uses:

1. Sentiment Analysis: BERT can effectively categorize text as positive, negative, or neutral sentiment. It works by examining individual words and the larger context of a sentence to determine the overall sentiment of the text.
2. Question-Answering: BERT can answer questions asked in natural language by scanning a large corpus of documents to find the answer.
3. Text Classification: BERT can classify text into predefined categories, such as spam or not spam, by learning and understanding word context.
4. Conversational AI: BERT can be used to create chatbots or voice assistants for natural language understanding and responses.
5. Language Translation: BERT can help in translating text from one language to another, as seen in Google Translate.
6. Search Engine: BERT can help search engines better understand the intent behind long-tail queries, improving the quality of search results to improve user experience.

Overall, BERT has become one of the most widely used natural language processing models, and its impact can be felt across a wide range of industries and applications, improving many aspects of our digital world.

GPT

The main idea behind the GPT model was to use unsupervised pre-training, where the model is trained on a large amount of unlabeled text data to learn the underlying patterns in the language. After pre-training, the model was fine-tuned on specific downstream tasks such as text classification or question-answering.

The first GPT model was a significant breakthrough in NLP and achieved state-of-the-art results on several language modeling benchmarks. Since then, OpenAI has released several improved versions of the model, including GPT-2 and GPT-3, which are among the largest and most impactful language models to date.

GPT-1?

GPT-1, was released in 2018 and had 117 million parameters. It was pre-trained on a large corpus of text data consisting of around 40GB of text from the Common Crawl dataset and was fine-tuned for various downstream natural language processing tasks, such as language translation and question answering.

Although GPT-1 is a significant breakthrough in the field of NLP, there are some limitations to the model that provide room for improvement. Some of the drawbacks of GPT-1 are:

1. Limited context awareness: GPT-1 is a unidirectional language model, which means that it does not consider the entire sequence of words in a sentence and only looks at the previous words. This limits its ability to capture and understand the context of a sentence in some cases.
2. Lack of fine-tuning options: GPT-1 was trained on a vast corpus of text from various domains. Fine-tuning the model on specific tasks requires additional training and fine-tuning procedures from scratch, which can take significant resources and time.
3. Fixed model architecture: The GPT-1 architecture has fixed hyperparameters that cannot be changed once it is trained. As a result, the model may not be optimized for certain tasks and may lack flexibility in handling new types of text data.
4. Limited Multimodal Representation: GPT-1 generates text-only outputs and lacks the ability to incorporate other modalities like images, sounds, and videos, which could be useful in some NLP applications.
5. Large computation requirement: GPT-1 is a large model, and training it requires significant computational resources, including large amounts of memory and GPU's. This limits its accessibility and applicability to some research and industrial organizations that lack the necessary hardware infrastructure.
6. Limited Contextual Encoding: GPT-1 uses WordPiece embeddings to represent tokens in the input sequence, which may not be suitable for all types of text data, and may lead to suboptimal results in certain contexts.

Overall, while GPT-1 was a milestone in natural language processing and improved language understanding, it still has its technical limitations that require improvement in future research.

GPT - 2

In 2019, OpenAI released GPT-2, an even larger model with 1.5 billion parameters. GPT-2 demonstrated impressive natural language generation capabilities, including the ability to write coherent and plausible prose with little or no prompting.

Here are some highlights of GPT-2:

1. Improved language modeling: GPT-2 is a larger and more powerful model than GPT-1, with 1.5 billion parameters compared to GPT-1's 117 million parameters. It demonstrated more robust language modeling capabilities and better performance in various natural language processing tasks.
2. Masked Language Modeling (MLM): GPT-2 uses MLM, a variant of the pre-training approach that involves randomly masking words in a sentence and training the model to predict the masked words, in addition to predicting the next word. The MLM objective improves the model's understanding of words in context and helps it to fill in the blanks more accurately.
3. Better Contextual Awareness: GPT-2 uses a bi-directional transformer architecture, which means that it considers the entire sequence of words in a sentence. This allows GPT-2 to better capture the meaning and context of a sentence, leading to more accurate predictions.
4. Few Shot Learning: GPT-2 demonstrated the ability to perform few-shot learning, which means that the model can learn to perform a task with only a few examples of annotated data. This makes it possible to fine-tune the model on specific tasks much more quickly and with fewer labeled data than required for conventional supervised learning methods.
5. Improved Text Generation: GPT-2 produces higher quality and more diverse text than its predecessor, demonstrating the potential to generate more human-like and coherent sentences for various natural language processing tasks.
6. Large Scale Applications: GPT-2's large scale and improved capabilities have led to its application in many large-scale NLP applications, including chatbots, text analysis, and web content generation.

Overall, GPT-2's improved modeling capabilities and enhanced contextual awareness provide better language interpretation, and have contributed significantly to advancing various NLP-related tasks.

While GPT-2 demonstrated significant improvements over its predecessor, there are still some limitations of the model:

1. Limited Interpretability: GPT-2 is a complex neural network with multiple layers, and it can be challenging to interpret how the model arrives at its outputs. This can lead to difficulties in understanding how the model works and may impede its use in certain industries or applications where interpretability is essential.
2. Large Model Sizes: GPT-2 is a large model, with 1.5 billion parameters. This makes it computationally expensive and difficult to deploy on low-end devices or in resource-restricted environments.
3. Ethical Concerns: GPT-2 demonstrated the ability to produce human-like language output, including generating fake news articles, which raised ethical concerns regarding the potential use of the model in creating biased, misleading or malicious content.
4. Over-reliance on Training data: GPT-2 is a pre-trained model and relies heavily on the quality and quantity of the training data used. The model may not perform well in domains with limited or different data sources.
5. Limited Multimodal Representation: Like its predecessor GPT-1, GPT-2 generates text-only outputs and lacks the ability to incorporate other modalities like images, sounds, and videos, which could be useful in some NLP applications.
6. Reproducibility: GPT-2 model training involves many hardware configurations, making it challenging to reproduce experimental results consistently.

Overall, while GPT-2 demonstrated significant improvements in natural language processing tasks, such as language modeling and text generation, it still has limitations related to interpretability, ethics, over-reliance on training data, and multimodal representations.

GPT - 3

GPT-3 (Generative Pretrained Transformer 3) is the successor of GPT-2, developed by OpenAI, which aims to create transformative breakthroughs in artificial intelligence and solve significant human-associated problems. GPT-3 was built in continuation to the same line of research that led to the development of its predecessor, GPT-2, but with some significant differences that gave rise to the new model.?GPT-3 was trained on the English Wikipedia, which is around 2 1/2 billion words, Common Crawl, WebText2, Books1, and Books2. Now

There were several reasons for the development of GPT-3, including:

1. Improved performance: OpenAI aimed to develop an even more powerful language model with better language understanding and text generation capabilities than GPT-2.
2. Balanced training set: GPT-3 was trained on a balanced dataset that contains a more comprehensive range of topics than its predecessor, focusing on web-text data from diverse domains, including scientific papers, news articles, and other forms of writing.
3. Few-shot Learning: OpenAI aimed to improve the ability of the model to perform well on new, previously unseen tasks with limited labeled data, leading to further improvements in few-shot learning capabilities.
4. Increased model size: GPT-3's model size was increased to 175 billion parameters, making it significantly larger than GPT-2. With this larger model size, the model can capture more complex information and improve its natural language generation capabilities.
5. Better Text Generation quality: GPT-3 aimed to generate text that was not only coherent but also grammatically correct, factual, and informative.

Overall, the idea that gave rise to GPT-3 was to create an even more powerful natural language processing model than its predecessor, GPT-2, with several enhancements and advancements that improved its text generation and language processing capabilities, and ultimately, the applicability of the model in different domains.

Despite its impressive performance and capabilities, GPT-3 still faces several challenges and shortcomings that require further research and development. Some of these challenges include:

1. Fine-tuning requirements: GPT-3 requires large amounts of data to fine-tune the model for specific natural language tasks. This can be a challenge for industries or organizations that have limited access to large and diverse datasets.
2. Lack of interpretability: GPT-3 is still a black box model, making it difficult to understand its decision-making process. This could lead to issues in transparency, accountability and interpretability, especially regarding its use in sensitive areas like financial or healthcare data.
3. Bias Infusion: The large-scale data used to train GPT-3 could be biased in many ways, for example, demographic, linguistic, and cultural bias. This can limit the fairness, accuracy, and inclusiveness of the model's output.
4. High computational requirements: Given the scale of the model, GPT-3 requires significant computing power and storage resources, making it difficult for small scale industries to use.

Overall, GPT-3 represents a significant step forward in natural language processing, but its challenges call for further research and development to improve its application in the real world. Advances in fairness, interpretability, and ethical consideration in AI, along with the use of non-textual sources of information, might help overcome some of these limitations.

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One of the other major concerns with GPT-3 and large language models is their environmental impact. A carbon emission study of large language models was conducted by Google and Berkeley in 2021 and found that training GPT-3 would've resulted in energy consumption of almost 1,300 megawatt hours and a release of 550 tons of CO2. We've looked at two of these shortcomings of GPT-3: bias and environmental impact. It's not surprising that some of the large language models that follow GPT-3 tried to optimize the model and address these challenges. And we'll take a look at some of them in the next blog.

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