|
|
|
|
|
by fernly
2339 days ago
|
|
I think he could have wrapped his paper up after showing this one example: > (input) I put two trophies on a table, and then add another, the total number is (GPT-2 continuation) five trophies and I'm like, 'Well, I can live with that, right? GPT-2 correctly inferred that the continuation should be a number of trophies, based on bazillions of similar sentences. But it had no understanding that arithmetic was called for. Despite the giant clues of "add" and "total", it didn't add 2+1 and continue "three trophies". It was mindlessly oblivious to the clearly implied request for a sum. Therefore it did not "understand" the input at all, in any sense whatever. |
|
I would (and I think anyone would) offer an operational definition: there is some class of questions to which this system could reply with sensible, actionable responses. Obviously the present system is not able to "understand" and answer simple arithmetic problems that a first-grader could answer instantly. Given that, would there be any point in expecting it to answer any other logical query that could be of use in one's work? (See the "medical" example in the article, about how to drink hydrochloric acid.)
The only question it appears to answer is, "given some words, what are other words that are likely to follow them in a typical blog post?" The fact that the words are syntactically correct is unimportant, when the fluent words convey no information relevant to the input.