4 ms·
Language models don't pick the top choice all the time, and even picking the top choice is some amount of information, more precisely -p_token*log(p_token). The
by visarga 4y ago
Language models don't pick the top choice all the time, and even picking the top choice is some amount of information, more precisely -p_token*log(p_token). They return a distribution we sample from. Sampling adds surprise and detail that was not originally there. Even more, if you have some sort of verification going - a second model to rate the outputs of the first, a math verifier or code executer when available - then language models can surprise us with interesting solutions to our tasks.
- toss1 4y agoThat is great when we are looking for interesting solutions. for example, it looks like GPT3-type systems might be very good search engines for obscure topics, getting them to finish paragraphs with their enormous search base. I've found a few tantalizing bits that I couldn't find on Google or DDG searches. But, when they are trying to express what WE are saying, it looks like a very lossy solution at best. I've had a deposition taken with an "AI" stenographer, and it was horrific, frequently reversing the meaning of sentences I said, or replacing an uncommon name with a common name (e.g., "John Kemeny" replaced with "Jack Kennedy"). Of course the transcript LOOKS great, it doesn't have any of the "(unintellegibile)" notations of a human transcript. It also does not go back at break points and ask for proper spellings of names, addresses, etc. like a human transcriber. This is in the context of a legal trial with consequences, and I'm horrified to see this kind of crap passing for usable products, and here we are looking to foist it off on the general public as writing tools. We're forking doomed by smart idiots looking to make a quick buck with novel "tools".