Large Language Models
Large language models are changing the face of AI and NLP. GPT-3, BERT, and T5 are models demonstrating the effectiveness of deep learning and transfer learning processing and creating human language. However, their use entails substantial ethical obligations, such as minimising biases and guaranteeing responsible AI development.
This glossary is your gateway to understanding large language models better.
What are large language models?
Large language models (LLMs) are a new type of artificial intelligence that can interpret and generate human language with extraordinary fluency and intricacy. Text datasets and code train large language models, enabling them to learn the patterns and relationships that underlie language. LLMs can potentially revolutionise many industries and applications, from customer service to creative writing.
Here are some of the key concepts and terms related to LLMs:
AI (artificial intelligence)
Artificial intelligence (AI) aims to construct intelligent machines. LLMs are one of AI research's most exciting and rapidly developing areas.
In machine learning, computers learn from data without being programmed. LLMs learn patterns and relationships in language through machine learning techniques.
Deep learning learns from data using artificial neural networks. Deep learning architectures form the foundation for LLMs, enabling them to process complex linguistic data.
Natural language processing (NLP)
NLP is an AI field that studies the interplay of computers and human language. LLMs are at the forefront of NLP research and have significantly advanced this field.
LLMs are typically pre-trained on a massive dataset of text and code. This pre-training process gives the model a broad understanding of language and its underlying patterns and relationships.
Once pre-trained, LLMs can undergo fine-tuning for specific tasks, such as text summarisation, question answering, or translation. This involves training the model on a smaller dataset of task-specific data.
Transfer learning is a key technique used to train LLMs. Transfer learning allows the model to leverage the knowledge it has learned from pre-training on general language data to learn new tasks more quickly and efficiently.
Tokenisation is breaking text into smaller units called tokens. LLMs typically use subword tokenisation, which splits words into subword components to handle different languages and vocabularies efficiently.
The transformer architecture is a key innovation that has enabled the development of LLMs. Transformers are neural network architectures that process sequential data, such as text.
The attention mechanism is a key component of the transformer architecture. The attention mechanism allows LLMs to focus on the most relevant parts of input text, essential for understanding context and relationships in language.
Word embeddings are numerical representations of words. LLMs use word embeddings to process and generate text.
Self-attention is a variant of the attention mechanism that allows LLMs to consider the importance of different words within the same sentence or paragraph. This is essential for understanding the context of a sentence and generating coherent and contextually relevant text.
When interacting with an LLM, you provide it with a prompt or input text. The prompt guides the model to generate the desired output. The prompt's quality and clarity can significantly impact the quality of the model's output.
Zero-shot learning is a remarkable capability of LLMs, allowing them to perform tasks they are not trained for. For example, an LLM trained on a text dataset and code can translate languages without being explicitly trained on a translation dataset.
Few-shot learning is another remarkable capability of LLMs that allows them to learn new tasks with minimal supervision. For example, an LLM can learn to classify new types of objects after being shown just a few examples of each object.
What are the applications of large language models?
LLMs have many applications, from chatbots and virtual assistants to content generation, translation, and summarisation. LLMs are transforming many industries by automating language-related tasks.
How can Infosys BPM help?
Infosys BPM’s generative ai for business can help clients implement and use large language models (LLMs) to improve their business. Infosys BPM has a deep understanding of LLM technology and its potential applications. We can help clients identify the areas of business where LLMs can improve efficiency, productivity, and customer satisfaction. Infosys BPM can help clients develop and implement custom LLM-based solutions and provide ongoing support and maintenance.