Thread this is about jumbled-up sentences, artificial intelligence, and language comprehension.…
Researchers at @AuburnEngineers and @AdobeResearch tried to get an natural language processing system to generate explanations for its behavior, such as why it claimed different sentences meant the same thing.
When they tested their approach they realized that shuffling words in a sentence made no difference to the explanations.
For example, the systems correctly spotted that the sentences “Does marijuana cause cancer?” and “How can smoking marijuana give you lung cancer?” were paraphrases.
But they were even more certain that “You smoking cancer how marijuana lung can give?” and “Lung can give marijuana smoking how you cancer?” meant the same thing too.
“This is a general problem to all [natural-language processing] models,” says @anh_ng8 at Auburn University, who led the work.
The models appear to pick up on a few key words in a sentence, whatever order they come in. They do not understand language as we do.
Moreover, GLUE—a very popular benchmark—does not measure true language use and teaches NLP models to jump through hoops. Many researchers have started to use a harder set of tests called SuperGLUE, but Nguyen suspects it will have similar problems.
The good news is that it might not be too hard to fix.
The researchers found that forcing a model to focus on word order, by training it to do a task where word order mattered, such as spotting grammatical errors, also made the model perform better on other tasks.
These results are yet another example of how artificial intelligence often falls far short of what people believe its capable of.…
.@anh_ng8 thinks the results highlight how hard it is to make AIs that understand and reason like humans. “Nobody has a clue,” he says.…
This story was reported and written by @strwbilly and turned into this Twitter thread by @Benji_Rosen. Read @strwbilly's full story here:…

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