The Enigma of LLMs: on Creativity, Compositionality, Pluralism, and Paradoxes

Yejin Choi (University of Washington / NVIDIA)
Special Year on Large Language Models and Transformers: Part 1 Boot Camp

About a year ago, I gave a talk at the Simons Institute workshop on "Large Language Models and Transformers" on "Possible Impossibilities and Impossible Possibilities". This talk is a follow-up on that talk, sharing my own experiences encountering divisive reactions from the research community, and following up on some of the open research questions with potentially confusing findings and even more open research questions. More concretely, I will touch on creativity, compositionality, and pluralism of LLMs, followed by generative AI paradoxes. The underlying thesis of my talk will be that it is time for us to acknowledge that we don't know about LLMs as much as some might like to assume we do, and that "Science of LLMs" is an important research direction to complement "Engineering of LLMs".

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GPT4 is not more creative than us, it was exposed to more ideas. The questions in this video are great, not that much the answers proposed.