The AI Revolution
The AI Revolution
Large language models (LLMs) are a type of artificial intelligence (AI) that can generate human-like responses by processing natural-language inputs. LLMs are trained on massive datasets, which gives them a deep understanding of a broad context of information. This allows LLMs to reason, make logical inferences, and draw conclusions.
LLMs are typically trained on a broad data set, possibly at the web scale. This is often too generic and possibly misses the domain-specific knowledge. Hence, users can find the output of LLMs impersonal for search or generative tasks. At the same time, it remains a challenge to eliminate bias and control offensive or nonsensical outputs.
LLMs acquire these abilities by using massive amounts of data to learn billions of parameters during training and consuming large computational resources during their training and operation. LLMs are artificial neural networks (mainly Transformers) and are (pre-)trained using self-supervised learning and semi-supervised learning.
LLMs use a long list of numbers called a “word vector” to represent words. Humans represent English words with a sequence of letters, like C-A-T for “cat.” Language models use a long list of numbers called a “word vector.” For example, here’s one way to represent cat as a vector: [0.0074, 0.0030, -0.0105, 0.0742, 0.0765, -0.0011, 0.0265, 0.0106, 0.0191, 0.0038, -0.0468, -0.0212, 0.0091, 0.0030, -0.0563, -0.0396, -0.0998, -0.0796…]. The full vector is 300 numbers long.
LLMs use statistical models to analyze vast amounts of data, learning the patterns and connections between words and phrases. This allows them to generate new content that is similar in style to a specific author or genre.
Most LLMs use a specific neural network architecture called a transformer which has some tricks particularly suited to language processing.
LLMs have been making extensive use of sites like Wikipedia and public forums as sources for their training data. OpenAI’s release of ChatGPT in November 2022 marked a significant advancement in terms of credibility, accessibility, and human-like output based on reinforcement learning from human feedback (RLHF). Subsequently, Google and Meta introduced their own LLMs expanding the possibilities with features like visual input and plugins.
The rapid increase in LLM applications has raised concerns about their potential misuse particularly in the medical domain. However, LLMs have found applications in patient care where effective communication is crucial. They can also be used in medical research where they can help with scientific content production. In medical education where the focus is on critical thinking and problem-solving LLMs can act as personalized teaching assistants.
LLMs are an exciting development in AI that has the potential to revolutionize many fields such as healthcare and education by generating human-like responses by processing natural-language inputs.
