From Supervised to Self-Supervised Learning: How Large Language Models are Transforming Machine Learning

18 min readNov 13, 2022

AI-Generated image of an ancient robot using Stable Diffusion.

Machine learning is the science and art of teaching machines to learn from data and perform tasks that humans can do. However, not all data and tasks are created equal, and different types of learning require different levels of human supervision and intervention. In this blog post, we will explore the concept of self-supervised learning, which is a paradigm that aims to reduce the need for human labels and annotations, and enable machines to learn from their own experience and context. We will focus on how self-supervised learning is applied to natural language processing (NLP), which is the field of machine learning that deals with understanding and generating natural language, such as text and speech. We will introduce the idea of language models, which are powerful neural networks that can learn to predict and generate language, and how they are trained and used for various NLP tasks. We will also discuss some of the applications and implications of large language models, which are language models that have billions or trillions of parameters and can capture a vast amount of linguistic and world knowledge. Finally, we will highlight some of the limitations and future directions of large language models, and how they can be improved and advanced to achieve more human-like and general intelligence.

Introduction

Machine learning can be broadly categorized into three types, depending on the amount and nature of human supervision involved: supervised, unsupervised, and self-supervised learning.

Supervised learning is the most common and familiar type of machine learning, where the machine learns from a set of labeled data, such as images with captions, or sentences with sentiments. The machine tries to learn a function that maps the input data to the output labels, and can then use this function to make predictions or classifications on new data. Supervised learning is very effective and widely used for many tasks, such as image recognition, speech recognition, sentiment analysis, and spam detection. However, supervised learning also has some drawbacks, such as requiring a lot of labeled data, which can be costly and time-consuming to obtain and maintain, and being prone to overfitting and generalization errors, which means that the machine may not perform well on data that is different from the training data.
Unsupervised learning is the type of machine learning where the machine learns from a set of unlabeled data, such as images without captions, or sentences without sentiments. The machine tries to learn the underlying structure, patterns, or features of the data, without any guidance or feedback from humans. Unsupervised learning can be useful for tasks such as clustering, dimensionality reduction, anomaly detection, and generative modeling. However, unsupervised learning also has some challenges, such as being difficult to evaluate and interpret, and being limited by the quality and diversity of the data.
Self-supervised learning is a type of machine learning that lies somewhere between supervised and unsupervised learning, where the machine learns from a set of data that has some form of supervision or signal, but this supervision or signal is not provided by humans, but rather derived from the data itself. For example, the machine can learn from images by predicting the missing pixels, or from sentences by predicting the missing words. The machine tries to learn a representation or a model of the data that can capture its semantics and context, and can then use this representation or model for other tasks that require human labels or annotations. Self-supervised learning can be seen as a way of leveraging the abundance and richness of unlabeled data, and generating artificial labels or tasks from the data, without relying on human intervention or feedback.

Self-supervised learning is especially relevant and important for natural language processing (NLP), which is the field of machine learning that deals with understanding and generating natural language, such as text and speech. Natural language is one of the most complex and expressive forms of data and communication, and it reflects the diversity and creativity of human culture and cognition. However, natural language is also one of the most challenging and elusive forms of data and communication, and it poses many problems and difficulties for machines, such as ambiguity, variability, inconsistency, and incompleteness. Moreover, natural language is also one of the most scarce and expensive forms of data and communication, and it is often hard and impractical to obtain and maintain large and high-quality datasets of labeled or annotated language, such as sentences with meanings, or paragraphs with summaries. Therefore, self-supervised learning can be a powerful and promising way of enabling machines to learn from the vast and varied sources of unlabeled or unannotated language, such as books, articles, blogs, tweets, reviews, and conversations, and to generate useful and meaningful labels or tasks from the language itself, such as predicting the next word, or filling in the blanks.

One of the most fundamental and influential concepts and applications of self-supervised learning for NLP is the idea of language models, which are neural networks that learn to predict the next word or token in a sequence of text. Language models can be seen as a way of modeling the probability distribution of natural language, and capturing the syntactic and semantic patterns and relationships of words and sentences. Language models can also be seen as a way of generating natural language, and producing coherent and fluent text that follows the rules and logic of language. Language models can be trained on large and diverse corpora of text, such as Wikipedia, news, books, or web pages, and can then be used for various NLP tasks, such as text generation, classification, summarization, translation, and more, with minimal or no fine-tuning.

However, training and using language models is not a trivial or easy task, and it involves many challenges and benefits, such as data, compute, and generalization. In the next section, we will provide an overview of these challenges and benefits, and how they affect the performance and potential of language models.

Large Language Models: What, Why, and How

Language models can vary in size and complexity, depending on the number and type of parameters and layers they have, and the amount and quality of data they are trained on. However, in recent years, there has been a trend and a race towards building and training larger and larger language models, which have billions or trillions of parameters and layers, and which are trained on massive and diverse datasets of text, such as the Common Crawl, which contains over 60 billion web pages. Some of the most popular and powerful examples of large language models are GPT-3 and BERT, which are considered to be the state-of-the-art and the benchmarks for many NLP tasks and applications.

But what are the advantages and disadvantages of large language models, and how are they different from smaller or simpler language models? And how are large language models trained and used, and what are the main components and techniques involved? In this section, we will try to answer these questions, and explain the what, why, and how of large language models.

What are large language models?

Large language models are language models that have a very high number of parameters and layers, which are the basic units and components of neural networks. Parameters are the weights and biases that determine how the neural network processes and transforms the input data, and layers are the groups and sequences of parameters that perform different operations and functions on the data, such as convolution, pooling, activation, or attention. The number of parameters and layers of a neural network can be seen as a measure of its size and complexity, and it can affect its capacity and ability to learn and generalize from the data.

For example, GPT-3, which is one of the largest and most advanced language models, has 175 billion parameters and 96 layers, and it is trained on 45 terabytes of text, which is equivalent to about 500 billion words. BERT, which is another large and influential language model, has 340 million parameters and 12 layers, and it is trained on 16 gigabytes of text, which is equivalent to about 3.3 billion words. In comparison, a typical smartphone has about 4 gigabytes of memory, and a typical human brain has about 86 billion neurons and 100 trillion synapses.

Why are large language models effective and versatile?

Large language models are effective and versatile because they can learn and capture a vast amount of linguistic and world knowledge, and they can perform various NLP tasks with minimal or no fine-tuning. Large language models can learn and capture a vast amount of linguistic and world knowledge, because they are trained on large and diverse datasets of text, which contain a lot of information and facts about language, culture, history, science, art, and more. Large language models can also learn and capture the syntactic and semantic rules and patterns of natural language, and how words and sentences are related and structured. Large language models can use this knowledge and understanding to predict and generate natural language, and to answer questions and queries that require reasoning and inference.

Large language models are also versatile and adaptable, because they can perform various NLP tasks with minimal or no fine-tuning. Fine-tuning is the process of adjusting and updating the parameters and layers of a neural network, based on a specific task or dataset, such as text classification or sentiment analysis. Fine-tuning can improve the performance and accuracy of a neural network, but it can also require a lot of labeled or annotated data, which can be scarce and expensive, and it can also cause overfitting and generalization errors, which means that the neural network may not perform well on data that is different from the fine-tuning data. Large language models can avoid or reduce the need for fine-tuning, because they can use their general and comprehensive knowledge and understanding of natural language, and apply it to different tasks and domains, without losing or compromising their quality and consistency. Large language models can also use different methods and techniques to adapt and customize their predictions and outputs, such as prompting, which is the process of providing some hints or cues to the language model, such as keywords or templates, to guide and influence its behavior and response.

How are large language models trained and used?

Large language models are trained and used by using different components and techniques, such as transformers, attention, pre-training, and fine-tuning. Transformers are a type of neural network architecture that is designed and optimized for processing sequential and structured data, such as text and speech. Transformers are composed of two main parts: the encoder and the decoder. The encoder takes the input data and converts it into a sequence of vectors, which are numerical representations that capture the features and meaning of the data. The decoder takes the sequence of vectors and generates the output data, such as the next word or token. Transformers are based on the concept of attention, which is a mechanism that allows the neural network to focus and attend to different parts of the input and output data, and to learn the relevance and importance of each part. Attention can help the neural network to deal with long and complex sequences of data, and to capture the context and relationships of the data.

Large language models are trained by using the technique of pre-training, which is the process of training the neural network on a large and general dataset of text, such as Wikipedia or Common Crawl, and using a generic and self-supervised task, such as predicting the next word or token, or masking and filling in the blanks. Pre-training can help the neural network to learn a general and robust representation and model of natural language, and to capture a vast amount of linguistic and world knowledge. Large language models are used by using the technique of fine-tuning, which is the process of adjusting and updating the parameters and layers of the neural network, based on a specific task or dataset, such as text generation or summarization. Fine-tuning can help the neural network to adapt and customize its predictions and outputs, and to improve its performance and accuracy. However, as we mentioned before, fine-tuning can also have some drawbacks and limitations, and large language models can sometimes perform well without or with minimal fine-tuning, by using methods and techniques such as prompting.

Applications and Implications of Large Language Models

Large language models have many applications and implications, both positive and negative, for various fields and domains, such as education, entertainment, business, health, and more. Large language models can enable and empower many new and innovative use cases and scenarios, that can enhance and improve the quality and efficiency of human-machine interaction and communication. However, large language models can also pose and create many ethical and social challenges and risks, that can harm and affect the privacy and safety of human-machine interaction and communication. In this section, we will showcase some of the most impressive and interesting applications and use cases of large language models, and we will also discuss some of the most critical and controversial implications and challenges of large language models.

Applications and Use Cases of Large Language Models

Large language models can be used for many different purposes and functions, such as generating, summarizing, translating, analyzing, and understanding natural language. Large language models can also be used for many different domains and topics, such as coding, art, music, and more. Here are some examples of the applications and use cases of large language models, that demonstrate their power and potential:

OpenAI Codex: OpenAI Codex is a large language model that can generate and execute computer code, based on natural language instructions or queries. OpenAI Codex can support various programming languages, such as Python, JavaScript, HTML, and CSS, and it can perform various coding tasks, such as creating web pages, games, apps, and more. OpenAI Codex is based on GPT-3, and it is trained on a large dataset of source code from GitHub and other sources. OpenAI Codex is the engine behind OpenAI Codex Playground, which is an online platform that allows users to experiment and play with OpenAI Codex, and to create and share their own projects and applications.
GPT-3 Playground: GPT-3 Playground is an online platform that allows users to access and use GPT-3, and to explore and discover its capabilities and features. GPT-3 Playground provides various templates and examples of how GPT-3 can be used for different tasks and domains, such as text generation, summarization, translation, classification, sentiment analysis, question answering, and more. GPT-3 Playground also allows users to create and customize their own prompts and queries, and to adjust and control the parameters and settings of GPT-3, such as the temperature, the top-p, the frequency penalty, and the presence penalty. GPT-3 Playground also allows users to save and share their results and outputs, and to browse and view the results and outputs of other users.

Implications and Challenges of Large Language Models

Large language models have many implications and challenges, both positive and negative, for various aspects and dimensions of human-machine interaction and communication, such as ethics, society, culture, and more. Large language models can enable and empower many new and innovative opportunities and benefits, that can enhance and improve the quality and efficiency of human-machine interaction and communication. However, large language models can also pose and create many ethical and social risks and harms, that can affect and damage the privacy and safety of human-machine interaction and communication. Here are some examples of the implications and challenges of large language models, that demonstrate their impact and influence:

Bias and Fairness: Bias and fairness are the issues and concerns related to the accuracy and equity of the predictions and outputs of large language models, and how they affect and reflect the values and preferences of different groups and individuals. Bias and fairness can be affected and influenced by various factors and sources, such as the data, the algorithms, the users, and the context. Bias and fairness can have various consequences and effects, such as discrimination, stereotyping, marginalization, and exclusion. Bias and fairness can also be measured and evaluated by various methods and metrics, such as accuracy, precision, recall, fairness, and diversity. Bias and fairness can also be addressed and mitigated by various strategies and techniques, such as data augmentation, debiasing, regularization, and accountability.
Privacy and Security: Privacy and security are the issues and concerns related to the protection and confidentiality of the data and information of large language models, and how they affect and respect the rights and interests of different groups and individuals. Privacy and security can be affected and influenced by various factors and sources, such as the data, the algorithms, the users, and the context. Privacy and security can have various consequences and effects, such as leakage, theft, misuse, and abuse. Privacy and security can also be measured and evaluated by various methods and metrics, such as encryption, authentication, authorization, and auditing. Privacy and security can also be addressed and mitigated by various strategies and techniques, such as anonymization, encryption, consent, and regulation.
Safety and Reliability: Safety and reliability are the issues and concerns related to the quality and consistency of the predictions and outputs of large language models, and how they affect and satisfy the expectations and needs of different groups and individuals. Safety and reliability can be affected and influenced by various factors and sources, such as the data, the algorithms, the users, and the context. Safety and reliability can have various consequences and effects, such as errors, failures, accidents, and harms. Safety and reliability can also be measured and evaluated by various methods and metrics, such as robustness, resilience, trust, and satisfaction. Safety and reliability can also be addressed and mitigated by various strategies and techniques, such as testing, debugging, monitoring, and feedback.

Limitations and Drawbacks of Large Language Models

Large language models have various limitations and drawbacks, such as scalability, efficiency, interpretability, robustness, and creativity. Here are some examples of the limitations and drawbacks of large language models, and how they affect and limit their performance and potential:

Scalability: Scalability is the issue and concern related to the ability and feasibility of training and using large language models, and how they affect and require the resources and costs of computation and storage. Scalability can be affected and influenced by various factors and sources, such as the data, the algorithms, the hardware, and the software. Scalability can have various consequences and effects, such as latency, complexity, and environmental impact. Scalability can also be measured and evaluated by various methods and metrics, such as speed, memory, and energy. Scalability can also be addressed and mitigated by various strategies and techniques, such as compression, pruning, distillation, and parallelization.
Efficiency: Efficiency is the issue and concern related to the performance and accuracy of large language models, and how they affect and utilize the resources and costs of computation and storage. Efficiency can be affected and influenced by various factors and sources, such as the data, the algorithms, the hardware, and the software. Efficiency can have various consequences and effects, such as redundancy, noise, and waste. Efficiency can also be measured and evaluated by various methods and metrics, such as accuracy, precision, recall, and complexity. Efficiency can also be addressed and mitigated by various strategies and techniques, such as regularization, optimization, quantization, and sparsity.
Interpretability: Interpretability is the issue and concern related to the understanding and explanation of the predictions and outputs of large language models, and how they affect and support the decisions and actions of different groups and individuals. Interpretability can be affected and influenced by various factors and sources, such as the data, the algorithms, the users, and the context. Interpretability can have various consequences and effects, such as confusion, uncertainty, and distrust. Interpretability can also be measured and evaluated by various methods and metrics, such as transparency, explainability, accountability, and trustworthiness. Interpretability can also be addressed and mitigated by various strategies and techniques, such as visualization, attribution, justification, and verification.
Robustness: Robustness is the issue and concern related to the quality and consistency of the predictions and outputs of large language models, and how they affect and handle the variations and uncertainties of the data and the environment. Robustness can be affected and influenced by various factors and sources, such as the data, the algorithms, the users, and the context. Robustness can have various consequences and effects, such as errors, failures, accidents, and harms. Robustness can also be measured and evaluated by various methods and metrics, such as reliability, resilience, trust, and satisfaction. Robustness can also be addressed and mitigated by various strategies and techniques, such as testing, debugging, monitoring, and feedback.
Creativity: Creativity is the issue and concern related to the originality and novelty of the predictions and outputs of large language models, and how they affect and reflect the values and preferences of different groups and individuals. Creativity can be affected and influenced by various factors and sources, such as the data, the algorithms, the users, and the context. Creativity can have various consequences and effects, such as boredom, plagiarism, and deception. Creativity can also be measured and evaluated by various methods and metrics, such as diversity, novelty, quality, and relevance. Creativity can also be addressed and mitigated by various strategies and techniques, such as sampling, exploration, inspiration, and evaluation.

Open Problems and Research Directions for Large Language Models

Large language models have various open problems and research directions, that can improve and advance their performance and potential, such as knowledge, reasoning, common sense, multimodality, and human-in-the-loop. Here are some examples of the open problems and research directions for large language models, and how they can improve and advance their performance and potential:

Knowledge: Knowledge is the issue and concern related to the acquisition and representation of the facts and information of large language models, and how they affect and support the predictions and outputs of large language models. Knowledge can be improved and advanced by various methods and techniques, such as knowledge bases, knowledge graphs, knowledge distillation, and knowledge injection. Knowledge can help large language models to enhance and enrich their predictions and outputs, and to answer questions and queries that require factual and specific information.
Reasoning: Reasoning is the issue and concern related to the inference and deduction of the logic and rules of large language models, and how they affect and support the predictions and outputs of large language models. Reasoning can be improved and advanced by various methods and techniques, such as logic, mathematics, symbolic, and neural. Reasoning can help large language models to improve and refine their predictions and outputs, and to answer questions and queries that require logical and analytical thinking.
Common Sense: Common Sense is the issue and concern related to the understanding and application of the common and general knowledge and beliefs of large language models, and how they affect and support the predictions and outputs of large language models. Common Sense can be improved and advanced by various methods and techniques, such as commonsense reasoning, commonsense knowledge, commonsense inference, and commonsense evaluation. Common Sense can help large language models to enhance and enrich their predictions and outputs, and to answer questions and queries that require common and general understanding and intuition.
Multimodality: Multimodality is the issue and concern related to the processing and integration of the different and diverse types and forms of data and information of large language models, such as text, speech, image, video, and audio. Multimodality can be improved and advanced by various methods and techniques, such as multimodal representation, multimodal fusion, multimodal generation, and multimodal evaluation. Multimodality can help large language models to expand and diversify their predictions and outputs, and to perform tasks and applications that require different and diverse types and forms of data and information.
Human-in-the-Loop: Human-in-the-Loop is the issue and concern related to the interaction and collaboration of the human and machine agents and actors of large language models, and how they affect and support the predictions and outputs of large language models. Human-in-the-Loop can be improved and advanced by various methods and techniques, such as human feedback, human evaluation, human guidance, and human oversight. Human-in-the-Loop can help large language models to improve and refine their predictions and outputs, and to perform tasks and applications that require human judgment, expertise, and creativity.

Conclusion

In this blog post, we have explored the concept of self-supervised learning, which is a paradigm that aims to reduce the need for human labels and annotations, and enable machines to learn from their own experience and context. We have focused on how self-supervised learning is applied to natural language processing (NLP), which is the field of machine learning that deals with understanding and generating natural language, such as text and speech. We have introduced the idea of language models, which are powerful neural networks that can learn to predict and generate language, and how they are trained and used for various NLP tasks. We have also discussed some of the applications and implications of large language models, which are language models that have billions or trillions of parameters and can capture a vast amount of linguistic and world knowledge. Finally, we have highlighted some of the limitations and future directions of large language models, and how they can be improved and advanced to achieve more human-like and general intelligence.

We hope that this blog post has given you a clear and comprehensive overview of the topic of self-supervised learning and large language models, and that you have learned something new and interesting. We also hope that this blog post has sparked your curiosity and interest in the topic, and that you will continue to explore and discover more about it. If you want to learn more about self-supervised learning and large language models, here are some suggestions and resources for further reading and learning:

Thank you for reading this blog post, and we hope that you have enjoyed it and found it useful and informative. We would love to hear your feedback and comments, and to know your thoughts and opinions on the topic. Please feel free to share your questions, suggestions, and ideas with us, and to join the discussion and conversation.