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Large Language Models: A short introduction
- A Large Language Model, or LLM, is a machine learning model that generates text based on the next word in a sentence with associated probabilities.
- LLMs are trained on enormous training datasets scraped from the internet to improve their ability to predict the next word with accuracy.
- The Large in LLMs refers not just to the size of the training data, but to the massive number of parameters that need to be adjusted.
- Transformers, initially developed by Google, is a new type of architecture that has allowed parallelization and attention operations in the LLM training process.
- LLMs have revolutionized a range of natural language and information retrieval tasks from translation to chatbots, to recommendation systems.
- They have been productized in a range of applications from Facebook's Meta AI to Google's Gemini and OpenAI's ChatGPT.
- LLMs are nascent, evolving at lightning speed and provide a wealth of opportunities for researchers and companies, and easier access to information for consumers.
- Genomic Language Models, or gMLMs, are also used to accelerate research on genome interactions - a task on which LLMs are proving particularly effective.
- LLMs have been highly successful so far, but improvement in their accuracy is essential to their future development and productivity.
- LLMs are tools that enable fast feedback loops making steady progress in science and helping companies provide customers ameliorated services.
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