Large Language Models: What They Are and Why They Matter

Language is one of the most powerful tools we have as humans. It allows us to communicate, learn, create, and express ourselves. But language is also complex and nuanced, and understanding it is not an easy task for machines.

That’s why researchers have been developing large language models (LLMs), which are artificial intelligence (AI) algorithms that use deep learning techniques and massively large data sets to understand, summarize, generate and predict new content based on natural language.

In this blog post, we will explore what LLMs are, how they work, what they can do, and what challenges they face.

What are LLMs?

LLMs are machine learning models that consist of a neural network with many parameters (typically billions of weights or more), trained on large quantities of unlabeled text using self-supervised learning or semi-supervised learning.

LLMs emerged around 2018 and perform well at a wide variety of tasks. This has shifted the focus of natural language processing (NLP) research away from the previous paradigm of training specialized supervised models for specific tasks.

Though the term large language model has no formal definition, it often refers to deep learning models having a parameter count on the order of billions or more.

LLMs are general purpose models which excel at a wide range of tasks, as opposed to being trained for one specific task (such as sentiment analysis, named entity recognition, or mathematical reasoning)14 The skill with which they accomplish tasks, and the range of tasks at which they are capable, seems to be a function of the amount of resources (data, parameter-size, computing power) devoted to them, in a way that is not dependent on additional breakthroughs in design.

Though trained on simple tasks along the lines of predicting the next word in a sentence, neural language models with sufficient training and parameter counts are found to capture much of the syntax and semantics of human language. In addition, large language models demonstrate considerable general knowledge about the world, and are able to “memorize” a great quantity of facts during training.

How do LLMs work?

LLMs are comprised of multiple layers of neural networks, which work together to analyze text and predict outputs. They’re also trained with a left-to-right or bidirectional transformer, which works to maximize the probability of following and preceding words in context — just like a human could reasonably predict what might come next in a sentence.

LLMs also have an attention mechanism that allows them to focus selectively on parts of text in order to identify the most relevant sections for summaries.

To train an LLM, one needs to identify a data set, which likely needs to be large in order for it to perform functions like a human, determine the network layer configuration, use supervised learning to learn the information in the data set, and finally fine-tune it based on performance or motive.

Some commonly used textual datasets for LLMs are Common Crawl, The Pile, MassiveText, Wikipedia, and GitHub. They run up to 10 trillion words in size. The stock of high-quality language data is within 4.6–17 trillion words, which is within an order of magnitude for the largest textual datasets.

What can LLMs do?

LLMs can perform a variety of natural language tasks that require understanding, summarizing, generating or predicting text or other content.

Some examples of these tasks are:

• Question answering: LLMs can understand questions and form meaningful responses based on their knowledge or information retrieval. For example, BERT (Bidirectional Encoder Representations from Transformers), developed by Google, can answer questions such as “Who is the president of France?” or “What is the capital of Australia?” by using Wikipedia as a source.
• Text summarization: LLMs can condense long texts into shorter summaries that capture the main points or highlights. For example, GPT-3 (Generative Pre-trained Transformer 3), developed by OpenAI, can generate summaries for news articles or academic papers by using attention mechanisms and language modeling techniques.
• Text generation: LLMs can produce coherent and diverse texts on various topics and styles, given a prompt or a context. For example, GPT-3 can generate summaries for news articles or academic papers, write essays or stories, compose emails or tweets, and even create code or lyrics.
• Text completion: LLMs can fill in the blanks or continue a text based on the previous content. For example, GPT-3 can autocomplete sentences or paragraphs, correct grammar or spelling errors, suggest keywords or hashtags, and infer missing information.
• Text classification: LLMs can assign labels or categories to texts based on their content or sentiment. For example, GPT-3 can classify texts as positive or negative, spam or not spam, relevant or irrelevant, and so on.
• Text extraction: LLMs can extract specific information or entities from texts based on a query or a pattern. For example, GPT-3 can extract names, dates, locations, numbers, keywords, and other relevant data from texts.
• Text translation: LLMs can translate texts from one language to another, preserving the meaning and the style. For example, GPT-3 can translate texts between English and other languages such as French, Spanish, German, Chinese, and more.
• Image generation: LLMs can create images from text descriptions, using a dataset of text–image pairs. For example, DALL·E (a 12-billion parameter version of GPT-3), developed by OpenAI, can generate images of various concepts expressible in natural language, such as “an armchair in the shape of an avocado” or “a store front that has the word ‘OpenAI’ written on it”.
• Image captioning: LLMs can describe images in natural language, using a dataset of text–image pairs. For example, GPT-3 can generate captions for images such as “a man riding a bicycle on a dirt road” or “a group of people sitting around a table with food and drinks”.
• Image completion: LLMs can regenerate any rectangular region of an existing image that extends to the bottom-right corner, in a way that is consistent with the text prompt. For example, DALL·E can complete images of animals or objects with missing parts based on their names or attributes.

These are just some of the examples of what LLMs can do with natural language and images. There are many more possibilities and applications that are yet to be explored and discovered.

What are the challenges of LLMs?

LLMs are impressive and powerful models that have achieved remarkable results in natural language processing and computer vision. However, they also face some challenges and limitations that need to be addressed.

Some of these challenges are:

• Data quality: LLMs rely on large amounts of data to learn language patterns and knowledge. However, not all data is reliable or accurate. Some data may contain errors, biases, inconsistencies, contradictions, or harmful content that can affect the performance and behavior of LLMs. Therefore, it is important to ensure that the data used to train LLMs is high-quality and representative of the intended domain and task.
• Computational cost: LLMs require enormous amounts of computational resources to train and run. A 2020 study estimated that the cost of training a model with 1.5 billion parameters can be as high as $1.6 million. However, advances in software and hardware have brought those costs down in recent years. Still, LLMs are not accessible to everyone and pose environmental concerns due to their energy consumption and carbon footprint. Therefore, it is important to find ways to optimize and reduce the computational cost of LLMs without compromising their quality and functionality.
• Generalization ability: LLMs are able to perform well on a wide range of tasks without requiring much task-specific training or fine-tuning. However, they may not be able to generalize well to new or unseen domains or tasks that are different from their training data. For example, LLMs may struggle to handle rare or novel words, concepts, or scenarios that they have not encountered before. Therefore, it is important to evaluate and test the generalization ability of LLMs and provide them with feedback and guidance when necessary.
• Ethical and social implications: LLMs have the potential to benefit society in many ways, such as enhancing education, communication, entertainment, and creativity. However, they also pose some ethical and social risks that need to be considered and mitigated. For example, LLMs may generate misleading, inaccurate, or harmful content that can affect people’s beliefs, opinions, or decisions. They may also infringe on people’s privacy, security, or intellectual property rights by using or producing sensitive or personal information. Moreover, they may create social and economic inequalities by displacing human workers or favoring certain groups over others. Therefore, it is important to ensure that LLMs are used responsibly and ethically, with respect for human values and dignity.

Conclusion

Large language models are a new and exciting frontier in artificial intelligence research and development. They have shown remarkable capabilities and performance in natural language processing and computer vision tasks, as well as in generating creative and diverse content.

However, LLMs also face some challenges and limitations that need to be addressed and overcome. They require high-quality data, computational resources, generalization ability, and ethical and social awareness to ensure their effectiveness and safety.

As LLMs continue to evolve and improve, we hope to see more applications and innovations that can benefit society and humanity in positive and meaningful ways.