> with temperature at zero, for the same input they produce always the same reply.
Nonsense.
https://thinkingmachines.ai/blog/defeating-nondeterminism-in...
> with temperature at zero, for the same input they produce always the same reply.
Nonsense.
https://thinkingmachines.ai/blog/defeating-nondeterminism-in...
BS. Run them sequentially on a single core, and without fancy speedups enabled, and they do. The algorithm is determinstic. Any non-determinism present with 0 temperature it's not some mysterious LLM-inherent property, but something that can be seen in any large program taking advantage of multi-core, floating point, and other CPU-based parallelism optimization.
Exactly, this is correct.
People often assume they are not because they can ask the same query to the same model and get differences in output, but wrongly conclude that this is some inherent LLM trait, instead of non-determinism added on top of it because of implementational choices that were made.
> Any non-determinism present with 0 temperature it's not some mysterious LLM-inherent property,
Of course it's not mysterious. It is well understood by all who are aware of the fundamental unreliability of all major LLMs in general use today.
The phrasing still makes it sound like it's due to the LLM. It's not. It's do to compiler and CPU, and OS optimizations, and a non-LLM program could suffer the same just as well.
This unreliability is entirely due to the implentation of the LLM. Yes, any other program implemented equally carelessly could suffer the same. But you'd be hard pressed to find any as unreliable as a typical LLM.