Large language models should be referred to as large event models because they are not only effective in language but also applicable in pharmaceuticals and various other fields.

Using large language models as prototyping tools can be effective; they allow for rapid development and testing of ideas before creating more specialized models tailored to specific tasks.

While there are significant potential benefits to large language models, we must tread carefully due to the ethical minefield they present.

Using large language models as judges, combined with human review, allows for scaling without overwhelming human subject matter experts.

Large language models (LLMs) are primarily designed to regurgitate information based on statistical patterns in the text they've been trained on, which limits their ability to invent new concepts or solutions.

The challenge of achieving truth with large language models is a contentious topic in the industry, especially in domains where definitive answers exist, like math and coding.

I believe we are nearing the upper limits of large language models, as the future will likely involve many smaller, specialized models rather than a few large ones. We're at the upper end of large language models, and they're rapidly becoming a commodity as they mature.

Evaluating the performance of applications built with large language models is challenging because the use cases are often subjective, making traditional accuracy metrics insufficient.

Words in large language models do not refer to meanings outside themselves; they are simply relations between symbols, which challenges traditional views of semantics and meaning in language.