Transformers & Large Language Models is concerned with models based on the transformer neural network architecture that introduced the attention mechanism and has become foundational in various machine learning applications. In addition, this concept includes Large Language Models, a specific application of transformers that are pre-trained on large amounts of textual data and excel at various natural language processing tasks.

Semantic Text Processing is a subfield of Natural Language Processing (NLP) that attempts to derive meaning from natural language and helps machines interpret textual data semantically. It involves developing algorithms and models that can understand and represent the semantics of words and sentences, and the relationships between them.

Natural Language Processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of 'understanding' the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

Language Models are a subfield of Semantic Text Processing that deal with the probabilistic modeling and generation of natural language, based on a learned probability function of sequences of words. Language models generate probabilities by training on corpora of natural language texts and can be fine-tuned for a variety of different downstream tasks.

We find limits to the Transformer architecture for language modeling and show it has a universal prediction property in an information-theoretic sense. We further analyze performance in non-asymptotic data regimes to understand the role of various components of the Transformer architecture, especially in the context of data-efficient training. We validate our theoretical analysis with experiments on both synthetic and real datasets.

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Transformers are Universal Predictors

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