That’s LLM bull. The model already knows hangman; it’s in the training data. It can introduce variations on the data, especially in response to your stimuli, but it doesn’t reinvent that way. If you want to see how it can go astray ask it about stuff you know very well, and watch how it’s responses devolve. Better yet, gaslight it. It’s very easy to convince LLMs that they’re wrong because they’re usually trained for yes-manning and non confrontation.
Now don’t get me wrong, LLMs are wicked neat, but they don’t come up with new ideas, but they can be pushed towards new concepts, even when they don’t grasp them. They’re really good at sounding sure of themselves, and can easily get people to “learn” new “facts” from them, even when completely wrong. Always look up their sources, (which Bard (Google’s) can natively get for you in its UI) but enjoy their new ideas for the sake of inspiration. They’re neat toys, which can be used to provide natural language interfaces to expert systems. They aren’t expert systems.
But also, and more importantly, that’s not zero-shot learning. Neat little anecdote from a conversation with them though. Which model are you using?
Update: I’ve tried the expert topics and gaslighting and the model was able to give expert level information but would always correct itself, if given new information, even though it seemed absurd.
However, the model would resist gas lighting for very well-known topics, such as claiming to be the “President of Mars”, it gave its logic for why the claim is false and was resistant to further attempts to try to convince it that this was true.
Overall, this was a good experiment in doing real world testing on a large language model.
Thanks for your suggestions – this is a problem that could be solved with future iterations of large language models! 💖
That’s LLM bull. The model already knows hangman; it’s in the training data. It can introduce variations on the data, especially in response to your stimuli, but it doesn’t reinvent that way. If you want to see how it can go astray ask it about stuff you know very well, and watch how it’s responses devolve. Better yet, gaslight it. It’s very easy to convince LLMs that they’re wrong because they’re usually trained for yes-manning and non confrontation.
Now don’t get me wrong, LLMs are wicked neat, but they don’t come up with new ideas, but they can be pushed towards new concepts, even when they don’t grasp them. They’re really good at sounding sure of themselves, and can easily get people to “learn” new “facts” from them, even when completely wrong. Always look up their sources, (which Bard (Google’s) can natively get for you in its UI) but enjoy their new ideas for the sake of inspiration. They’re neat toys, which can be used to provide natural language interfaces to expert systems. They aren’t expert systems.
But also, and more importantly, that’s not zero-shot learning. Neat little anecdote from a conversation with them though. Which model are you using?
deleted by creator
Update: I’ve tried the expert topics and gaslighting and the model was able to give expert level information but would always correct itself, if given new information, even though it seemed absurd.
However, the model would resist gas lighting for very well-known topics, such as claiming to be the “President of Mars”, it gave its logic for why the claim is false and was resistant to further attempts to try to convince it that this was true.
Overall, this was a good experiment in doing real world testing on a large language model.
Thanks for your suggestions – this is a problem that could be solved with future iterations of large language models! 💖