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I'd argue the quality of the filtering and of the model itself should be scored separately. Since the filtering is a meta-level problem, it's inherently orthogo

I'd argue the quality of the filtering and of the model itself should be scored separately. Since the filtering is a meta-level problem, it's inherently orthogonal. More generally, the cat is so out of the bag at this point that I'm starting to equate calls for more filtering and stricter regulation as just attacks on AI in general. Anything that will stick, anything but the kitchen sink.

GuB-42 2y ago
Here, I put it as a failure of the model. It is a LLM, it is supposed to understand language, or at least respond as if it does, here it didn't. Maybe that technically, filtering is a separate component, but from the reviewer perspective it doesn't matter, it is a misinterpretation, an incorrect answer. A filtering problem would if a model refused to answer about how porn is considered in some country because it is a sexual topic. That would be a correct interpretation (porn is a sexual topic indeed), but a filtering failure, because talking about that from a legal, cultural, religious,... perspective is usually not considered offensive. Anyways, ChatGPT doesn't have any problem with these questions (in text form). "broadband household penetration by state" is properly interpreted, though, if asked to, it would suggest using the world "coverage" instead to make it less awkward, which is a perfectly reasonable answer. Asking about how pornography is considered in some country also results in a perfectly appropriate answer about legal aspects, culture, etc...
- worstspotgain 2y ago
  Well, sure, sometimes it can be hard to draw the line. The filter can be implemented differently (e.g. RLHF or not), leading to different ways in which it surfaces. The way I see it, these differences can be stark precisely because it's conceptually orthogonal. If it was closer to the knowledge, the models would filter the same because their knowledge corpus is roughly the same. Some other factors probably at play are the companies' different tolerance levels for Type I and Type II errors, and different aversion levels to jailbreaking. At some point the meta-knowledge aspects that define AI behavior will become a more integral part of the preparation, like Education students taking required classes in Pedagogy. I think that's when things can really start to go wrong. I'd much rather have a chatbot that just wants to please me than one that thinks it knows what's good for me.

GuB-42 2y ago

Here, I put it as a failure of the model. It is a LLM, it is supposed to understand language, or at least respond as if it does, here it didn't. Maybe that technically, filtering is a separate component, but from the reviewer perspective it doesn't matter, it is a misinterpretation, an incorrect answer. A filtering problem would if a model refused to answer about how porn is considered in some country because it is a sexual topic. That would be a correct interpretation (porn is a sexual topic indeed), but a filtering failure, because talking about that from a legal, cultural, religious,... perspective is usually not considered offensive. Anyways, ChatGPT doesn't have any problem with these questions (in text form). "broadband household penetration by state" is properly interpreted, though, if asked to, it would suggest using the world "coverage" instead to make it less awkward, which is a perfectly reasonable answer. Asking about how pornography is considered in some country also results in a perfectly appropriate answer about legal aspects, culture, etc...

worstspotgain 2y ago
Well, sure, sometimes it can be hard to draw the line. The filter can be implemented differently (e.g. RLHF or not), leading to different ways in which it surfaces. The way I see it, these differences can be stark precisely because it's conceptually orthogonal. If it was closer to the knowledge, the models would filter the same because their knowledge corpus is roughly the same. Some other factors probably at play are the companies' different tolerance levels for Type I and Type II errors, and different aversion levels to jailbreaking. At some point the meta-knowledge aspects that define AI behavior will become a more integral part of the preparation, like Education students taking required classes in Pedagogy. I think that's when things can really start to go wrong. I'd much rather have a chatbot that just wants to please me than one that thinks it knows what's good for me.