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LLMs tell bad jokes because they avoid surprises

danfabulich.medium.com

31–40 of 163 posts

Re: LLMs tell bad jokes because they avoid surprises

#31
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.

Re: LLMs tell bad jokes because they avoid surprises

#32
post #15

This sounds really convincing but I'm not sure it's actually correct. The author is conflating the surprise of punchlines with their likelihood. To put it another way, ask a professional comedian to complete a joke with a punchline. It's very likely that they'll give you a funny surprising answer. I think the real explanation is that good jokes are actually extremely difficult. I have young children (4 and 6). Even 6…

That's true. You would think LLM will condition its surprise completion to be more probable if it's in a joke context. I guess this only gets good when model really is good. It's similar that GPT 4.5 has better humor.

Which is notable, because GPT-4.5 is one of the largest models ever trained. It's larger than today's production models powering GPT-5.

Goes to show that "bad at jokes" is not a fundamental issue of LLMs, and that there are still performance gains from increasing model scale, as expected. But not exactly the same performance gains you get from reasoning or RLVR.

Re: LLMs tell bad jokes because they avoid surprises

#33
One time I was playing around with LLaMA and I injected Senator Stephen Armstrong (with me inputting his lines) into a mundane situation. In response to "I'm using war-as-a-business so I can end war-as-a-business", the model had one of the characters conclude "oh, he's like the Iron Sheik of politics!", which got an honest chuckle out of me. I don't follow wrestling, so I don't know if it's an appropriate response, but I found it so random that it was just funny.

Re: LLMs tell bad jokes because they avoid surprises

#34

This sounds really convincing but I'm not sure it's actually correct. The author is conflating the surprise of punchlines with their likelihood. To put it another way, ask a professional comedian to complete a joke with a punchline. It's very likely that they'll give you a funny surprising answer. I think the real explanation is that good jokes are actually extremely difficult. I have young children (4 and 6). Even 6…

[dead]

Re: LLMs tell bad jokes because they avoid surprises

#37
post #2

The fact that any surprise used in the wrong place is considered hallucination and a downside for that LLM. I guess a good starting point to improve that is to add an experimental "Surprise Mode" which will try to guess the right kinds of surprises rather than minimizing them and get the feedback from the users. Over time, it will learn what kind of surprises users like so that they will be used in future training da…

Hallucinations aren't surprising, that's why they're problematic. They tend to look like exactly what you'd expect to be true, they just aren't.

Re: LLMs tell bad jokes because they avoid surprises

#38

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: “Do…

> 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.

Re: LLMs tell bad jokes because they avoid surprises

#39
This post got me wondering, so I asked chatgpt for jokes about hackernews as if made by various comedians...I laughed at one or two /shrug

Bill Hicks (angry truth-telling prophet)

"Hacker News is where ideas go to die. Not because they’re bad — but because 400 anonymous software prophets decided they’re God. And God doesn’t code, man."

---

Bill Burr (ranty everyman, annoyed at everything)

"I went on Hacker News once. Jesus. It’s like walking into a bar where everyone’s smarter than you, but still somehow an idiot. ‘Actually, you should’ve used Rust.’ Actually, you should shut the hell up, Todd."

---

Louis C.K. (brutal honesty, self-deprecation)

"I asked Hacker News if my startup idea was good. And they said, ‘No, because you’re an idiot.’ And I was like… yeah, that’s fair. My idea was basically Tinder but for people who hate me. Which is… everyone."

---

George Carlin (sharp language, hypocrisy calling)

"Hacker News. Don’t you love that name? Hacker. News. Two lies in two words. Nobody’s hacking, and it sure as hell ain’t news. It’s just nerds arguing over tabs and spaces like it’s the f*ing Middle East."

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Tom Segura (laid-back storyteller, dry punch)

"So my buddy posts his app on Hacker News, right? He’s all excited, like, ‘Dude, we’re gonna blow up.’ By comment three: ‘This is trash, here’s a 40-page essay why you should quit.’ He calls me crying. I’m like, yeah man, that’s the feature, not the bug."

---

Jimmy Carr (short, brutal, wicked flip)

"I posted on Hacker News for feedback. They gave it to me. Turns out suicide is an option."

Re: LLMs tell bad jokes because they avoid surprises

#40
This is a great way to express it. In the past I tried to express the same idea to non-techies by saying models generate an average of their inputs, which is totally wrong. But this way to explain it is much better.

In fact the training process is all about minimizing "perplexity", where perplexity is a measure of how surprised (perplexed) the model is by its training data. It's some exponential inverse of the loss function, I always forget the exact definition.

With enough parameters the models are able to mix and match things pretty well, so the examples of them generating funny jokes aren't necessarily a great rebuttal as there are so many jokes on the web and to find them requires nearly exact keyword matching. A better observation is that we haven't heard many stories of LLMs inventing things. I feel I read about AI a lot and yet the best example I can come up with was some Wordle-like game someone got GPT4 to invent and that was a couple of years ago.

I've found this to be consistently true in my own work. Any time I come up with an algorithm or product idea I think might be novel, I've asked a model to suggest solutions to the same problem. They never can do it. With some leading questions the smartest models will understand the proposal and agree it could work, but they never come up with such ideas cold. What they think of is always the most obvious, straight line, least common denominator kind of suggestions. It makes sense that this is because they're trained to be unsurprising.

Fixing this is probably the best definition of AGI we're going to get. Being surprising at the right time and unsurprising at others is one of the hardest things to do well even for people. We've all known the awkward guy who's learning how to be funny by just saying as much weird stuff as possible and seeing what gets a reaction. And in the corporate environment, my experience has been that innovative people are lauded and praised when they're inventing a golden goose, but shortly after are often demonized or kicked out. The problem being that they keep saying surprising things but people don't like being surprised, especially if it's an unpleasant surprise of the form "saying something true but unsayable", e.g. I don't want to work on product X because nobody is using it. What most people want is a machine that consistently generates pleasant surprises and is a personality-free cog otherwise, but that's hard for even very intelligent humans. It's often hard even to want to do that, because personality isn't something you can flick on and off like a lightswitch. A good example is how Mark Zuckerberg, one of the most successful executives of our era, would have been fired from his own company several times already if he didn't control the voting shares.

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