They are weights for matrix operations, so in principle you'd think they would be deterministic. In practice it's more complicated than that.
Not to be snarky or dismissive, I mean this genuinely: ask an LLM about it. I currently have a headache so I'm not up to explaining the technical details, but they are interesting and worth reading about.
Achieving determinism with LLMs and other neural network models is actually a hard problem that people spend a lot of time on, when they need that. It doesn’t happen by accident.
Issues include accumulated floating point errors happening in different orders due to distributed and parallel computation, CUDA kernels that deliberately sacrifice determinism for speed, and several other such issues.
It happened that I am working on OSS LLM -> finetuning -> benchmark with 100k tests pipeline, and unless I do some data augmentation, result is 100% deterministic.
I think you likely right, that some parts of stack could induce some marginal float point error, but converged model can mitigate it, and on some principal set of knowledge can give deterministic result with high probability.
Which leads me to believe if you give this task to Anthropic, who has very strong incentive, they will build such benchmark, and then can tell that benchmark gives correct answer with 99.9% probability and it will be enough to drag someone to court.
They are weights for matrix operations, so in principle you'd think they would be deterministic. In practice it's more complicated than that.
Not to be snarky or dismissive, I mean this genuinely: ask an LLM about it. I currently have a headache so I'm not up to explaining the technical details, but they are interesting and worth reading about.
Achieving determinism with LLMs and other neural network models is actually a hard problem that people spend a lot of time on, when they need that. It doesn’t happen by accident.
Issues include accumulated floating point errors happening in different orders due to distributed and parallel computation, CUDA kernels that deliberately sacrifice determinism for speed, and several other such issues.
It happened that I am working on OSS LLM -> finetuning -> benchmark with 100k tests pipeline, and unless I do some data augmentation, result is 100% deterministic.
I think you likely right, that some parts of stack could induce some marginal float point error, but converged model can mitigate it, and on some principal set of knowledge can give deterministic result with high probability.
Which leads me to believe if you give this task to Anthropic, who has very strong incentive, they will build such benchmark, and then can tell that benchmark gives correct answer with 99.9% probability and it will be enough to drag someone to court.