Gregory's Server@simon @carlmjohnson I guess I also object to this term because it doesn’t really have a bug—it isn’t really “malfunctioning” as I put it either. The goal that it’s optimizing towards is “believability”. Sycophancy and sandbagging are not *problems*, they’re a logical consequence and a workable minimum-resource execution of the target being optimized. It bugs me that so much breathless prose is being spent on describing false outputs as defects when bullshit is *what LLMs produce by design*
|
2 comments
 @simon @carlmjohnson it worries me a little bit that I, with just like, a passing familiarity with what gradient descent is and how ML model training works, can easily predict each new PR catastrophe and “misbehavior” of these models and the people doing the actually phenomenally complex and involved work of building them seem to be constantly blindsided and confused by how the tools that *they are making* behave |
@simon @carlmjohnson if it accidentally wastes resources telling the truth where a more-compressible lie or error would have satisfied the human operator, that’s a failure mode! It will eventually be conditioned out in future iterations, although an endless game of whack-a-mole will ensue as they try to pin it down to *particular* “test” truths (which is exactly what “sycophancy” is) while all others decay