Large Language Models: The Basics You Need to Know

In recent years, Large Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP) by enabling machines to understand and generate human-like text. These models are powered by highly advanced artificial intelligence (AI) and machine learning techniques. Their popularity is steadily growing as they have paved the way for innovative applications such as text prediction, AI-driven content generation, and more. During the last few years, enterprise interest in LLMs has sky-rocketed following the emergence and rise of generative AI applications like ChatGPT. This is the reason why modern enterprises, and their CIOs (Chief Information Officers) must understand the fundamentals of LLMs, their capabilities, as well as some of the most prominent examples of LLMs.

Large Language Models Background

Before delving into large language models, it is essential to understand the broader context of AI-driven text prediction and content generation. Traditionally, rule-based systems and statistical methods were employed in NLP, but had limitations in capturing the intricacies of human language. The advent of neural networks in NLP (e.g., deep learning for language processing) brought about significant progress, leading to the emergence of LLMs. These models utilize machine learning algorithms and massive amounts of data to train and fine-tune their language generation skills. Hence, they can process and generate human-like text.

To train an LLM, researchers feed it massive amounts of text data, which the model uses to learn the statistical relationships between words and phrases. Once trained, the model can use this knowledge to generate coherent and contextually appropriate text in response to user inputs. For example, when building a chatbot that can help customers with their online orders, an LLM is trained on a large corpus of customer support chat logs to learn the patterns and nuances of customer queries. The LLM can then use this knowledge to generate responses to customer queries. Most importantly, it gradually improves its performance over time as it receives more training data.

Why LLMs Now?

Given that relevant NLP research has been around for several decades, many people wonder why LLMs were only recently used in real-life applications. This is because some key factors have contributed to their delayed development. One of the primary reasons is the requirement of significant computational power and resources to train and run these models. LLMs are characterized by their massive size, often consisting of billions to trillions of weights and parameters. Thus, the practical deployment of LLMs asks for large amounts of computational resources and advanced AI accelerators that can process vast amounts of text data. The availability and accessibility of such large-scale computational resources have improved over time, which has enabled the development and practical use of LLMs.

During the last few years, we have also seen the advent of transformer-based architectures. These architectures introduced new neural network structures and techniques, which played a significant role in advancing LLMs. In particular, the ‘transformer’ architecture revolutionized natural language processing by effectively capturing contextual information and dependencies between words.

Note also that the development of LLMs relied on the accumulation and organization of extensive textual data, primarily sourced from the internet. Collecting and curating such large and diverse datasets is a complex and time-consuming process, which was hardly possible during past decades.

Overall, the availability and accessibility of sufficient data, along with the computational power to process it, were key factors that had to converge for LLMs to be developed effectively. It is also worth noting that the progression of LLMs builds upon the collective advancements in machine learning, artificial intelligence, and natural language processing research and development over the years. As these fields matured, researchers gained valuable insights and techniques that contributed to the development and successful deployment of LLMs.

LLMs Applications

Some of the most popular applications of LLMs include:

• Text Prediction with AI: This refers to the task of suggesting or completing a sentence or phrase based on the given context. Large language models excel at this task, as they can estimate the most probable word or phrase continuation, thereby generating coherent and contextually appropriate text. For instance, when composing an email, these models can suggest the next word as you type, which makes the process more efficient.
• AI-driven Content Generation: One of the most fascinating applications of LLMs is AI-driven content generation. These models can generate high-quality and human-like text in various domains, including news articles, poetry, code, and more. By analyzing massive amounts of text data, they learn to mimic the style and tone of different authors, leading to content that is nearly indistinguishable from human writing.

Current Limitations of LLMs

While large language models have made significant progress in understanding and generating human-like text, they are not without their limitations. Here are some of the key challenges that researchers face in developing and deploying large language models:

• Data Bias: One of the biggest concerns with large language models is data bias. These models are trained on massive amounts of text data collected from various sources, which can unintentionally reinforce existing biases. For instance, if a model is trained on a corpus of text that contains primarily male authors, it may generate biased or gendered language. Researchers are working towards mitigating this issue by carefully curating training datasets and testing models for potential biases.
• Computing Power Requirements: Due to their massive size and complexity, LLMs require high amounts of computational resources to train and run. This often poses a challenge for researchers who may not have access to specialized hardware or cloud computing resources. However, advancements in computing technology are making these models more accessible to researchers and developers. Likewise, nowadays many LLMs come with data-efficient AI capabilities (e.g., few-shot learning) that reduce the computational resources required for their deployment and use.
• Lack of or Limited Explainability: Another limitation of large language models is the lack of sufficient explainability or interpretability. LLMs work through complex algorithms that are not easily interpretable by humans, making it difficult to understand how decisions are being made. This lack of explainability can impede regulatory compliance and user trust, as it’s hard to know why the model is making certain predictions or generating certain text.
• Low Resource Languages: Large language models require a rich source of data to be effective, which can be a challenge for languages that are not as widely used or documented. Developing models that can effectively analyze and generate text in low resource languages is an active research area. There is a need to explore new approaches to representing languages to create better models.
• Resource-Intensive Fine-Tuning: While pre-trained models like BERT and GPT-3 can perform a wide range of tasks, fine-tuning them for specific applications can require significant resources. As a result, some smaller organizations or non-tech companies may not have the resources to fine-tune these models to their specific requirements.

Examples of Prominent LLMs

Some of the most prominent and popular LLMs include:

• Bidirectional Encoder Representations from Transformers (BERT): BERT is one of the most influential large language models developed by Google. BERT models have been pre-trained using a large corpus of text, allowing them to understand the context and meaning of words based on surrounding words. They set new benchmarks in various NLP tasks such as question-answering, sentiment analysis, and text classification.
• GPT: GPT 3.5 is based on OpenAI’s Generative Pre-trained Transformer-3 (GPT-3). It is an incredibly powerful language model. Trained on a colossal dataset. GPT-3 can generate contextually coherent and meaningful paragraphs, essays, and even programming code. It enables developers to leverage the power of AI without extensive programming knowledge, making it a versatile tool for various content generation tasks. Nowadays, GPT 4, the next generation of this Generative Pre-trained Transformer has been also released and is already very widely used.
• Bard: Google’s Bard is another very impressive LLM. It is designed to generate poetry in various styles and themes, demonstrating the potential for AI to create artistic and emotive content. Bard showcases the expressive capabilities of LLMs while pushing the boundaries of AI creativity.

By and large, LLMs have brought about a significant transformation in NLP algorithms and techniques, enabling machines to leverage language generation models that generate human-like text and understand language in unprecedented ways. With applications ranging from text prediction to AI-driven content generation, these models have become an invaluable resource across industries. Examples like BERT, GPT 3.5, and Bard illustrate the remarkable capabilities of LLMs and of next generation NLP model architectures, showcasing their proficiency in tasks like text prediction, content generation, and creativity.