USING PROBABILITY PRINCIPLES OF GENERATIVE ARTIFICIAL INTELLIGENCE IN LANGUAGE MODELS

Abstract

Currently, language models (in particular, large language models, such as GPT chat) are widely used in practice, as they satisfy various user needs: writing articles, communicating with users, and so on. Language models are a new direction in artificial intelligence that enables the creation of flexible and adaptive systems. Thanks to the development of language models, developers, entrepreneurs, and users can create their own applications based on existing models, using AI for various tasks. The work of generative AI is based on probabilistic principles. The article analyzes the features of the application of probabilistic principles of generative artificial intelligence in language models. In modern generative artificial intelligence, language models learn to generate text by predicting the next words (tokens) based on a probability distribution. Understanding how the probability distribution of tokens is formed and how it can be manipulated (via softmax, temperature, sampling truncation) is key to tuning the behavior of a language model - from pragmatically accurate responses to creatively unexpected ones. Metrics such as entropy, KL-divergence, and perplexity allow us to analyze these distributions. They provide tools for quantifying the uncertainty and knowledge of a model, for comparing the model to a reference distribution or another model, and for tracking the progress of training. New approaches, such as diffusion models, demonstrate that it is possible to abandon step-by-step token prediction and still generate coherent text. However, in these models too, probabilities play a central role, guiding the process of adding and removing noise. Therefore, the emphasis on a probabilistic approach, understanding the probability distribution of tokens, is the foundation for the further development of generative artificial intelligence. This combines mathematical precision with practical methods for improving the quality and diversity of generated content.