#Issue 24 - Brief timeline of LLM development
The last two years have likely seen the fastest development of large language models (LLMs), driven by the exponential growth of GPU power and the vast amounts of data available on the internet. LLMs have now penetrated a wide range of analytical and creative tasks. However, the foundation for this rapid growth was primarily established with the development of the Transformer architecture in 2017. Even before that, researchers had been working on various language models. Let's explore the timeline of how computers have learned to process language.
The next challenge lies in working on the weaknesses of LLMs. Bias, hallucination, and sensitivity to input phrasing, which can lead to inaccurate or unfair outputs. Additionally, they are resource-intensive, lack true common sense and context understanding over long texts, and pose ethical and privacy concerns.
That’s it folks. Thanks for reading!