Over the last decades, multiple developments in the field of natural language processing (NLP) have resulted in achieving large language models (LLMs).
To understand LLMs, let’s first explore the statistical techniques for NLP that over time have contributed to the current techniques.
The beginnings of natural language processing (NLP)
As NLP is focused on understanding and generating text, most first attempts at accomplishing NLP were based on using the rules and structure inherent to languages. Especially before machine learning techniques became prevalent, structural models and formal grammar were the primary methods employed.
These approaches relied on explicit programming of linguistic rules and grammatical patterns to process and generate text. Though these models could handle some specific language tasks reasonably well, they faced significant challenges when confronted with the vast complexity and variability of natural languages.
Instead of hard-coding rules, researchers in the 1990s began to utilize statistical and probabilistic models to learn patterns and representations directly from data.
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