That's only true assuming you habe enough data to train a domain-specific model / expertise to train it and test it correctly.
I've encountered cases where an image recognition task could be accomplished well with a very general model like CLIP, but people still fine-tuned another model on their own small data set because that's considered better.
A domain specific model might be more likely to fail on weird outliers not present in the small domain specific training data.
> could spell disaster for OpenAI
Nah I don't think so. They are not all in on one specific model architecture. If the current architecture is found to have serious unfixable flaws then they'll just change architecture.
This is not even close to true for Language models.
Wait, am I misunderstanding you? I feel like I've had a head injury or something, because I've never heard of an open source LLM that's as capable as GPT-4 (in most scenarios).
The LLM space is just different. There's no guarantee a fine-tuned model will beat a bigger generalist one.
The LLM space is just different. There's no guarantee a fine-tuned model will beat a bigger generalist one.
I think it groups the batch up differently, so if I have a batch of 10, and it groups it up into 2 groups of 5, if my prompt makes it to the second group or 1st group I get a different answer. But if I’m in the same location in the batch, then I get the same answer.
The whole batch is deterministic given the same batch (sequences and ordering), but if you shuffle the batch then you lose that determinism.
Is it saying that part of its more-efficient inferencing relies on mixing tokens from completely-separate inputs – eg, from other users? And then, depending on what other inputs chance into the same grouping, the relative assignment-to-'experts' varies, and thus the eventual completions?
If so, I'd see that as not just introducing non-determinism, but also potentially making the quality of your responses dependent on how-many-concurrent-requests are fighting for the same expert-allocations.
(For example, maybe the parts of the system best at translating/interpreting Hindi give worse results during peak usage hours-of-the-day in India, when the most concurrent inputs are competing for that same competence.)
Perhaps also, this is another possible explanation for perceived quality-degradation over time. When certain tests were reliably succeeding earlier, there was less congestion for the relevant 'experts'. Now, with more concurrent use, those same tests aren't as reliably winning as much of relevant 'experts' effort.
This may also suggest a bit of a quagmire: on whatever domains some sub-experts seem impressively good, initially, even more proportionate use will be attracted. But such new congestion means all the copycat use no longer gets the same expert allocations – and thus the initially-impressive performance degrades.
(And if the effect is strong, & known-but-undisclosed-by-OpenAI, does it amount to a bait-and-switch? Attract users with unrepresentative excellence on an initially-uncongested Mixture-of-Experts system, but then offer them the lower-quality results from a more-congested system.)