Screenshot of this question was making the rounds last week. But this article covers testing against all the well-known models out there.
Also includes outtakes on the ‘reasoning’ models.
Screenshot of this question was making the rounds last week. But this article covers testing against all the well-known models out there.
Also includes outtakes on the ‘reasoning’ models.
Well, they are language models after all. They have data on language, not real life. When you go beyond language as a training data, you can expect better results. In the meantime, these kinds of problems aren’t going anywhere.
See, that’s not even an accurate criticism because part of language is meaning. This test is a test of an LLM having enough “intelligence” to understand that you can’t wash your car without your car being at the car wash. If you see the language presented in this test and don’t immediately realize that it would be a problem, then you haven’t understood the language. These are large language models failing at comprehending any language. Because there’s no intelligence there. Because they’re just random word guessers.
Why act like this is an intractable problem? Several of the models succeeded 100% of the time. That is the problem “going somewhere.” There’s clearly a difference in the ability to handle these problems in a SOTA models compared to others.