I propose that we adopt the term "Large Self-Supervised Models (LSSMs)" as a replacement for "Foundation Models" and "LLMs". "LLMs" don't capture non-linguistic data and "Foundation Models" is too grandiose. Thoughts? @percyliang

@tdietterich The beauty of language is that you can have multiple terms that highlight different aspects of the same object. You don't have to choose. I use "LLM" to talk about LLMs, "self-supervised" for their construction, and "foundation model" for their function. No term can be replaced.

@percyliang Yes, but as you know, "Foundation" is too close to "Foundational", and many of us find that troubling. That is why I'm proposing a more neutral term. For use, maybe we could just call them "Upstream models".

@giffmana @tdietterich @percyliang A causal auto-regressive model certainly gets a lot of bits of supervision with one cross-entropy score per token, with zero ml-specific cost for labeling.

@francoisfleuret @tdietterich @percyliang That's correct, but orthogonal to the discussed point.

@giffmana @tdietterich @percyliang My point was that *in principle* self-supervision can provide far more "training bits" than standard supervision, hence should eventually dominate.