alt Hacker NewsWhat is "drift"? It seems to be one of those words that LLMs love to say but it doesn't really mean anything ("gap" is another one).
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This definition satisfied me: https://universalpaperclips.fandom.com/wiki/Value_Drift
I believe it's businessspeak for "change." Gap is suittongue for "difference."
there are many causes, but it’s a drift in performance
you can drift a tool via the harness in many ways
you can modify the system prompt
you can modify the underlying model powering the harness
you can use different “thinking” levels for different processes in the harness
you can change the entire way a system works via the harness, which could be better or worse, depending on many things
you can introduce anti-anti-slop within the harness to foil attempts from users using patch scripts
you can modify how your tool sends requests to your server depending on many variables
you can handle requests differently, depending on any variable of your choosing, at the server level
you can modify the compute allotment per user depending on many things, from the backend, without telling the user, it’s very easy. you can modify it dynamically depending on your own usage or the user’s cycle. Or their organization’s priority level as a customer. The weekly and daily usage management system is intricate, compute is very finite and must be managed
the user has literally no way to know and you have no legal obligation to tell them, you never made them any legally binding promises
the combination of so many factors that all affect each other means that you can, if you’d want to, create a new clusterfuck of an experience anytime any of these or unknown variables change, it may not even be deliberate, it grows exponentially complex, so you may not even be able to promise a specific standard to your users
drift is not imagined, sure, but admitting to it could expose you to unneeded liability
IDK how it applies to LLMs but the original meaning was a change in a distribution over time. Like if you had some model based app trained on American English, but slowly more and more American Spanish users adopt your app; training set distribution is drifting away from the actual usage distribution.
In that situation, your model accuracy will look good on holdout sets but underperform in user's hands.