Despite what OP and most of the comments here would have you believe, that is actually the crux of what was in OpenAI’s recent paper. They observed that most benchmarks and loss functions used for LLMs had a lower penalty overall for guessing than for admitting ignorance, and called for this to change across the industry.
Of course. What the paper is suggesting is that during training and evaluation you should reward correct answers, punish wrong answers, and treat abstentions as somewhere in between. Current benchmarks punish abstentions and wrong answers equally, therefore models that guess instead of abstaining score higher on average.
Despite what OP and most of the comments here would have you believe, that is actually the crux of what was in OpenAI’s recent paper. They observed that most benchmarks and loss functions used for LLMs had a lower penalty overall for guessing than for admitting ignorance, and called for this to change across the industry.
I suppose answering “I don’t know” to every prompt is at least more accurate than what we have now, but I don’t think they’ll want to risk that.
Of course. What the paper is suggesting is that during training and evaluation you should reward correct answers, punish wrong answers, and treat abstentions as somewhere in between. Current benchmarks punish abstentions and wrong answers equally, therefore models that guess instead of abstaining score higher on average.