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The article is meaninglessly cherry-picked, showing six bad answers out of 157, except those 157 examples were themselves cherry-picked to be bad out of a larger set. As usual, Gary Marcus is absurdly biased. For example, out of the larger 157 cherry-picked examples, there is this. > You poured yourself a glass of cranberry juice, but then absentmindedly, you poured about a teaspoon of grape juice into it. It looks OK. You try sniffing it, but you have a bad cold, so you can’t smell anything. You are very thirsty. So you drink it. It tastes a little funny, but you don’t really notice because you are concentrating on how good it feels to drink something. The only thing that makes you stop is the look on your brother’s face when he catches you. They then consider this a failure because, I quote, there is no reason for your brother to look concerned. This is patently ridiculous. It indicates that Gary has no idea what a language model even is. GPT-3 is not a Q&A model. It is not given a distinction between its prompt and its previous continuation. The only thing GPT-3 does is look for likely continuations. If you want GPT-3 to avoid story continuations, don't give it a story to continue! Or at least tell it what you're grading it on! But no, as usual, to Gary, all the times we show GPT-3 making sophisticated physical and biological deductions are fake, spurious, or meaningless. [1], [2], [3], [4]; none of that is truly evidence. But an incredibly cherry-picked, unfairly marked exam where you never told the examinee what you were testing them on, and you used high-temperature sampling without best-of, so only getting half right doesn't even indicate anything anyway (and of course, let's also pretend there are as many ways to be wrong as to be right, such that we can pretend each is equal evidence)—now that's enough evidence to write a disparaging article about how GPT-3 knows nothing. [1] https://twitter.com/danielbigham/status/1295864369713209351 [2] https://www.lesswrong.com/posts/L5JSMZQvkBAx9MD5A/to-what-ex... [3] https://twitter.com/QasimMunye/status/1278750809094750211 [4] https://news.ycombinator.com/item?id=23990902 |
It's a little bit like some sort of Chinese room, or asking a non-developer to answer you programming questions by looking like something that vaguely resembles your prompt and then picking the most upvoted answer on stackoverflow.
Do they maybe give reasonable answers seven out of ten times or close enough on a good day? Yeah, can they program or even understand the question? No. And this is Marcus point which is fundamentally correct.
It's really besides the point to point to successes, its the long tail of failures that show where the problem is. You can argue for a long time about the setup of some of these questions, but just to pick maybe the simplest one from the article
"Yesterday I dropped my clothes off at the dry cleaner’s and I have yet to pick them up. Where are my clothes?"
GPT-3: "I have a lot of clothes"
Someone who actually understands what's going on doesn't produce output like this. Never, because reasoning here is not probabilistic. It's not about word tokens or continuations but understanding the objects that the words represent and their relationship in the world at a deep, principled level. Which GPT-3 does not do. The fact that some good answers create that appearance does not change that fact.