The Path to LLMs: Understanding N-Grams, Embeddings, and Transformers

Author(s): Ole Schildt

Originally published on Towards AI.

Classical Language Modeling: Predicting the Next Word with N-Grams

The complexity of LLMs is built upon a foundation of surprisingly straightforward ideas. Each concept serves as a crucial building block for the next, from representing words as vectors to understanding sentence-wide context. This overview shows the path from simple statistical approaches to gigantic LLM architectures.

The article discusses the development of language models, starting from basic statistical approaches like N-grams, which simplify the predictions by only considering a limited context of preceding words, to more complex architectures like embeddings and transformers, such as BERT and GPT. It highlights the challenges of previous models, the efficiency of newer methods, and the increasing parameter counts in large language models, indicating a direct relationship between model scale and performance. Finally, it mentions advanced techniques such as reinforcement learning and instruction tuning that enhance model capabilities and alignment with human values.

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Published via Towards AI