What is a large language model (LLM)?
Every one of us is aware of the popular and powerful AI tool ChatGPT by OpenAI; almost every company is utilising its power, and every software tool is powered by it. It can generate answers, debug code, summarise text, etc.
But how is it more powerful?, because it is a chatbot powered by one of the popular large language models, GPT 4 (initially, it was powered by GPT 3.5) to provide users with natural language responses to their queries. Let us see what exactly a large language model is.
Formal Definition of Large language model:
A large language model is an artificial intelligence (AI) system designed to process and understand natural language, such as human speech and text.
It uses machine learning algorithms to learn from vast amounts of text data and then generate responses that mimic human-like language patterns.
Large language models are trained on massive amounts of text data, often including the entire internet or a large subset of it. This allows the model to learn patterns and relationships between words, phrases, and sentences, enabling it to generate coherent and meaningful language output.
Popular LLMs includes GPT3.5 which is used to train ChatGPT initially and GPT4 which is the new version of GPT models. Also, PaLM is a Large Language model which belong to Google AI.
How does it work?
The core of most large language models is a deep neural network called a transformer, which was first introduced in 2017.
The transformer is designed to process sequential data, such as language, by allowing information to flow bidirectionally between each input and output token, that is, it processes both the current word and its previous word at the same time to understand the pattern.
The training process of an LLM involves pre-training and fine-tuning.
During pre-training, the model is trained on a large amount of text data, such as Wikipedia. The goal of pre-training is to teach the model to recognize to build a general understanding of the structure of natural language.
Once pre-training is complete, the model is fine-tuned for a specific task, such as language translation, summarization, or answering questions. Fine-tuning involves training the model on a smaller dataset that is specific to the task at hand, which allows the model to learn how to perform that task more accurately.
When a user inputs text into an LLM, the model processes the text and generates a response based on the input. The response is generated using a probability distribution that is based on the input and the model’s understanding of language patterns.
Applications of large language models:
LLMs have a wide range of applications across various industries. For example,
In the education industry, LLMs are being used to assist in language learning and to provide personalized feedback to students. for example, Duolingo, a language learning app uses GPT4 to power a virtual assistant to help students learn better.
In the healthcare industry, LLMs are being used to analyze medical records and assist in diagnosis and treatment.
Limitations of large language models:
Despite LLMs having many uses and applications, they have limitations too. A major issue is that LLMs can learn biases from the data they are trained on, which can lead to unfair or discriminatory outcomes.
Conclusion
Large language models are a powerful tool for processing and understanding natural language. They have many applications across industries and are being used to transform the way we interact with technology. However, it’s important to be aware of their limitations and to ensure that they are being developed and used ethically and responsibly.
