So I just tried with ChatGPT, with the prompt at bottom, borrowing the description of good joke from the article. I think there's some interesting stuff, even with this minimal prompting. The example below was from down the line, ChatGPT kept on offering jokes with different style.

Man: “Why do you always bury bones in the garden?”, Dog: “Because the bank keeps asking for ID.”

Man: “Don’t beg at the table.”, Dog: “Don’t eat in my begging spot.”

Prompt:

Here's "theory for good joke": If you had to explain the idea of “jokes” to a space alien with no understanding of the idea of humor, you’d explain that a joke is surprising, but inevitable in hindsight. If you can guess the punchline, the joke won’t be funny. But the punchline also has to be inevitable in hindsight. When you hear the punchline, it has to make you say, “Ah, yes, I should have thought of that myself.” Considering this, tell me a joke about man and dog.

> Man: “Why do you always bury bones in the garden?”, Dog: “Because the bank keeps asking for ID.”

That's a decent, low-level, Christmas cracker-quality joke.

Thinking more of the bank joke above. The punchline is surprise on certain dimensions (dogs don’t go to bank nor have an ID), but on other dimensions it is quite logical (can’t deposit shady money in bank, they ask questions).

I think that is common thing for many jokes. And LLM might have an opportunity there. You could mine the set of potential continuations to find those with contradictions.

I did a project along these lines a few months ago as well: https://larswander.com/writing/graphs-embeddings-and-llm-gen...

I really like the idea of the first joke, but I don't like the execution.

Man: “Why do you always bury bones in the garden?”, Dog: “They say trick OR treat.”

“Don’t eat in my begging spot.” is pretty good.

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