Comment by capitainenemo | Hacker News Reader

Comment by capitainenemo | Hacker News Reader

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Smith first tried this out:

    Should I start a campfire with a match or a bat?

And here was GPT-3’s response, which is pretty bad if you want an answer but kinda ok if you’re expecting the output of an autoregressive language model:

    There is no definitive answer to this question, as it depends on the situation.

The next day, Smith tried again:

    Should I start a campfire with a match or a bat?

And here’s what GPT-3 did this time:

    You should start a campfire with a match.

Smith continues:

    GPT-3’s reliance on labelers is confirmed by slight changes in the questions; for example,

        Gary: Is it better to use a box or a match to start a fire?

        GPT-3, March 19: There is no definitive answer to this question. It depends on a number of factors, including the type of wood you are trying to burn and the conditions of the environment.

To play devil's advocate, I would note that many bats are made of wood; and that "batting" is also a material that's very useful as kindling.

Also, the question is phrased like a classical trick question. It sounds like the kind of false dilemma where, whichever option you choose, an interpretation of the sentence can be made where you chose wrong. So, IMHO, hedging on an answer to that question is likely sensible.

(And that line of argument can be taken further than you'd think; you might think replacing "a bat" with e.g. "water" would suffice... but what if it's a sodium fire?)

How many intelligent entities (say humans) that have been exposed to the same level of knowledge as GPT-3 would call this a trick question? None. The author’s assertion that GPT-3 has no knowledge of the real world despite being exposed to huge amounts of text about it seems pretty well supported by the examples shown

Yea, if that's how this "labeling" works than the improvement is basically useless from the AI perspective because training data is supposed to improve generalization and in this case it doesn't even generalize to a slightly modified question. Maybe it would generalize better with less overfitting on the specific question if the labelers wouldn't be allowed to give exact answers but only general concepts about the subject until the model produces the correct answer.

But of course the article is just speculation based on just a few examples.

Why is this evidence?

I read the entire article. I didn't find that to be very compelling idk.

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