Summarizing LLMs

This article provides a brief overview of the history of Large Language Models (LLMs), starting from the rule-based era. It highlights the limitations of early systems like ELIZA, which relied on manually written rules and struggled with the ambiguity of language. The article points out the scalability issues and the inability of these systems to handle unexpected inputs. It correctly identifies the conclusion that manually writing all the rules is not a feasible approach for creating intelligent language processing systems. The article is a good starting point for understanding the evolution of LLMs and the challenges faced by early AI researchers.

• •Early LLMs relied on manually written rules.
• •Rule-based systems struggled with ambiguity and unexpected inputs.
• •Manually writing all rules is not a scalable solution for language processing.