Large language models or LLMs are artificial intelligence or specific natural language models based on deep learning algorithm that can recognize, summarize, translate, predict, and generate human-like texts and even other content such as images and audio based on knowledge from large datasets.
The concept is called “large” because the specific model is trained on a massive amount of text data. The training dataset has allowed a particular LLM to perform a range of language tasks such as language translation, summarization of texts, text classification, question-and-answer conversations, and text conversion into other content, among others. Furthermore, because of possible varied applications, several LLMs are also considered as foundation models.
Several developments in artificial intelligence applications have been made possible through the introduction of large language models. Companies such as Google, Microsoft, and OpenAI have been at the forefront of LLM research and development.
Understanding Better What Large Language Models Are: The Purpose and Applications of LLMs
Purpose of LLMs: What Does It Do and How Does It Work?
Explaining Language Models and How It Relates to Artificial Intelligence
A language model is a probability distribution over sequences of words. It is specifically a statistical model that is trained on a huge corpus of text data to predict the likelihood of a sequence of words in a specific language. It specifically works by assigning a probability to the whole sequence of words.
This model is considered a type of artificial intelligence and one of the positioned solutions to problems involving computational linguistics. It uses machine learning techniques, specifically deep learning algorithms, to make predictions based on learned patterns and relationships in a given training dataset made of texts.
Note that there are two types of language models. These are generative models and discriminative models. Generative language models generate text and other related content based on the learned language patterns and with language input. Discriminative language models analyze and sort a particular text into pre-defined categories.
Large Language Models and Their Role in Natural Language Processing
Natural language processing or NLP is one of the goals and fields of artificial intelligence. It is specifically concerned with giving computer systems the capabilities to read, comprehend, interpret, and generate written texts and spoken words in the same manner as humans can. The purpose of NLP is to equip computer systems with language processing abilities.
Language models are essential in NLP. Furthermore, due to the demands for advancing natural language processing, large language models or LLMs are considered fundamental to developing and introducing next-generation and wide-scale NLP applications. LLMs have also been considered to define innovative and practical AI applications.
What set LLMs apart from standard language models are the size of their training datasets and the actual size of a particular model itself. This is similar to comparing the differentiating capabilities of data analytics to big data analytics. These models are trained on enormous amounts of text data and their sizes reach tens of gigabytes.
The size of large language models has allowed them to capture complex patterns in the language and make predictions with high accuracy. The development and introduction of newer LLMs are central to NLP research. These models are also regarded as essential tools in revolutionizing NLP and enabling novel and useful NLP applications.
Applications of LLMs: Who are Involved and What are the Examples?
Key Organizations Involved in Developing Large Language Models
The most notable organization involved in advancing LLMs and NLP research is the American AI research lab OpenAI. This company has been credited for deploying one of the largest large language models in the world: the autoregressive and transformer-based language model called generative pre-trained transformer or GPT and the GPT-3 model.
Other tech companies have also been involved in LLM research and development. Google has introduced its transformer-based LLM in 2019 called the Bidirectional Encoder Representations from Transformers or BERT. The company has integrated this model into its search engine technology to increase the capabilities of its Google Search to understand human language and improve the results of search queries.
The Large Language Model Meta AI or LLaMA is another LLM from Meta released in February 2023. It is positioned as a foundational model developed on large dataset rather than parameters or model size and designed to help researchers advance their work in natural language processing and other related artificial intelligence applications.
Notable Applications of LLMs and Known LLM Products
ChatGPT is one of the known services that demonstrate the capabilities of a large language model. This is a chatbot from OpenAI based on its GPT models that can produce human-like textual conversations. It can even produce answers based on specific prompts and generate short-form to long-form texts, compose lyrics of music, write fiction, and write or debug codes.
One of the most known applications of language models is in the retrieval of information using the query likelihood model. This application is seen in web search engines and built-in search engines of certain websites and databases. Virtual assistants such as Siri from Apple, Alexa from Amazon, and Bixby from Samsung are based on language models.
The more practical applications of large language models have exploded since 2010. Writing assistants such as Grammarly and Quillbot are based on LLMs. These models have also been applied in generative AI and AI agents. Generative AI products include content generators, programming automation, AI-based virtual assistants, and non-human researcher, among others.