UCLA researchers’ new study says the GPT-3 language model can reason with analogy, but they can’t say the answers aren’t already in the training data.
to the AI. They also asked the bot to solve a set of SAT analogy questions that involved word pairs. Say a vegetable is related to a cabbage. Therefore, an insect is equivalent to a “beetle,” and so on. The researchers claimed that, to their knowledge, the questions had not appeared on the internet and that it was “unlikely” it would have been gobbled up as part of GPT-3’s training data. Again, the AI performed at a level slightly above the average meat bag.
was overall better at the task. Asked to use a bunch of household objects to transfer gumballs from one room to another, and the AI came up with “bizarre solutions.” Webb and his fellows have been working on this problem for close to half a year, and since their initial preprint they’ve added more tests to the model. All these tests led them to start openly theorizing about how GPT-3 could be forming some kind of “mapping process” similar to how humans are theorized to tackle such problems.
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