|
No, it is not too much to ask in the specific case of Gebru’s bad paper. Several of the arguments are specious, like comparing the total energy consumption for training GPT with car trips, or demanding that NLP researchers have to keep up with rapidly changing activist “woke” vocabulary and ensure their models are respecting it. These are ridiculous claims, and it’s fair to respond to them by saying, “well, what exactly do you imagine a solution or mitigation looks like?” Essentially, by the nature of how specious Gebru’s stated problems are, they demand clarity over what an “ethical solution” even is, conceptually, and why everyone would have to agree. For example, you could discuss economies of scale or train-once-finetune-everywhere approaches with GPT that reduce total energy needs. Or you could discuss how researchers can register the corpus they use and the snapshot of time it was grabbed, with an open understanding that as long as the methods and data are reproducible, there is no research ethical issue with studying that corpus, no matter how much bias or lack of woke vocab a given person believes it has. (And also, nobody is required to just accept activist language as important or valid.) Gebru did none of this. The article could literally be summed up by Gebru saying, “I think <supposedly shocking evidence> is bad, therefore its connection to something in ML is bad.” E.g. “I think, subjectively, that the raw energy use to train GPT is bad. Here are some shocking comparisons. Therefore GPT is bad.” It’s incredibly unrigorous and juvenile. Dean’s comments that it needs to clearly state mitigations is actually a super generous, polite way of saying the paper is just subjective amateur hour. |