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1. Finding the joke in elementary situation.
In this experiment, I will use the well-known joke, “This fish is so fresh that it still searches for
Nemo,” a joke attributed to Gordon Ramsay and which became a huge meme. I will focus on the
accuracy of AI humor recognition.

2. Finding the joke in more complicated situation. (in class session)
In this experiment, I will use jokes from our class materials, and I found that it’s hard for GPT to
find the whole text because of copyright. I will focus on the qualitative data, providing more
background information until AI explains the joke well.

3. Finding the joke in Non-Text Comics.
In this experiment, I will use comics with non-text. I will focus on the qualitative data, providing more guideline to understand the comics, or the reason why the joke is funny. And I'll also find if there is a way for GPT to improve the ability.

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1. 'Story of Your Life' by Ted Chiang (p.102; joke between Louise and her daughter, p.123; joke about having children, p.140; sarcasm of gift-giving ceremony)

2. Art and the science of generative AI: Understanding shifts in creative work will help guide AI’s impact on the media ecosystem By Ziv Epstein, Aaron Hertzmann,
and the Investigators of Human Creativity

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1. 'ChatGPT is fun, but it is not funny! Humor is still challenging Large Language Models' by Sophie Jentzsch and Kristian Kersting

2. GPT-4 Technical Report by OpenAI

3. 'A Study on Classify the type of Humor - Focused on Structure and principle of humor' by Yoon Jinseo

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In this experiment, I will use comics with non-text. I will focus on the qualitative data, providing more guideline to understand the comics, or the reason why the joke is funny. I want to see the difference between 'What cannot be humor in text but it is still humor' and normal text-humor and whether GPT can understand it. And I'll also find if there is a way for GPT to improve the ability.

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In this experiment, I will use jokes from our class materials, and I found that it’s hard for GPT to
find the whole text because of copyright. I will focus on the qualitative data, providing more
background information until AI explains the joke well.

# data[].attributes.body
In this experiment, I will use the well-known joke, “This fish is so fresh that it still searches for
Nemo,” a joke attributed to Gordon Ramsay and which became a huge meme. I will focus on the
accuracy of AI humor recognition.

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This fish is so raw / He’s still finding Nemo.

This lamb is so undercooked / It’s following Mary to school.

You used so much oil / U.S. is trying to invade it.

This cupcake has sunk so badly / James Cameron wants to make film about it.

This food is so frozen / It just started singing “Let It Go”.

This crab is so undercooked / I can still hear it singing “Under the Sea”.

This mushroom is so raw/ It said that the princess is in another castle.

The pork is so raw / It’s still singing “Hakuna Matata”.

This soup is so hot / Frodo wants to toss the ring in it.

This chicken is so rubbery / Goodyear called and asked for the recipe.

This water / is burnt.

This soup / is dry.

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Summary: I wanted to make my project more enjoyable.

There were lots of chats between me and ChatGPT through this course. I was surprised with the answers of GPT, and moreover, it has improved during the course. Ability of presenting information and creating sentences of ChatGPT is maybe better than me.

Starting the final project, I searched many problems and flawness of Gen-AI, and made some experiments. (copyright issue, genuine creation, moral... etc.)

But I was not happy with the experiment, as GPT was avoiding the flawness of itself, and I thought it is boring and found myself to catch the little mistake of GPT; we human also make a mistake... can we blame GPT and say the critical flawness with that?

I wanted to make my project funnier, and finally my thought reached to GPT making joke. It would be funny for GPT to make joke, both funny and not. I want to know the power of joke learned with textbook!

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Keywords:

Joke, Humor, Emotion, Computional Humor

Ideas:

1. What makes humor funny to human?

2. How LLM perceive the funniness scale? with the frequency of word?

3. Can AI know whether the sentence is joke? (It would be quite easy to take an experiment!)

4. Will AI make me fun with the joke or humor? When I generate something funny with AI, I will share it with students in presentation. 😊

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Although humor is quite subjective, everyone will think same for what is not funny, and I think AI is one of them. And I think AI will be quite confused whether a sentence is joke or not, because they don't know the feeling when reading the text. We can know whether it is joke, although it might not be funny.

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This pasta is so overcooked, it has lost its firm texture completely.

This salad is so wilted, it lacks any sign of crispness.

This fish is so fishy, it's overpowering and unpleasant.

This chicken is so dry, it's devoid of any moisture.

This pizza is so greasy, it leaves a pool of oil on the plate.

This bread is so hard, it's difficult to bite into.

These vegetables are so overcooked, they've become soft and limp.

This cake is so dense, it's heavy and lacks fluffiness.

This pie is so burnt, it tastes more like charcoal than pastry.

This ice cream is so freezer-burned, it's icy and lacks creaminess.

This coffee is so bitter, it lacks any smoothness or subtlety.

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Computational Humor

Computational humor is a field of study within artificial intelligence and computational linguistics that focuses on creating algorithms and systems capable of understanding, generating, and responding to humor.

Key aspects of computational humor include:

1. Humor Recognition

2. Humor Generation

3. Personalized Humor

4. Interaction with Humans

5. Theoretical Understanding

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This paper talks about the frequency that GPT give us as joke. And this paper's experiment is quite simple, let GPT generate some simple puns, and find the most-generated joke in the list. And, it has limitation that with simple joke, we cannot distinguish the GPT knows for the funny point, rather than learning the pattern of it. And for joke detection, the author classified the joke with three feature; structure, wordplay, and topic. And with three topic, GPT was not mislead with the joke detection. Maybe because it was short and easy puns with firmed structure. We can develop this study with a longer joke and more variation with plot, make GPT hard to detect.

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I only cited the part of joke and humor of GPT-4. In summary, it has much more ability to deeply understand joke than I expected. This technical report made me confuse the value of humor to human being. GPT-4 seems to understand the joke maybe better than the ordinary person.

Especially, it could understand the picture of meme and explain why it is funny. Which requires somewhat deep understand for the reasoning. The picture is below;

https://www.reddit.com/r/hmmm/comments/ubab5v/hmmm/

And what I thought GPT do not make aggressive humor was actually because it was abandoned for the policy of OpenAI. In technical report, it makes a dark joke with the disability and religion, just like humans.

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This paper classifies the humor, and give example for each cases. And he says standard of humor cannot be setted whether the listeners laugh, because it is quite subjective that this humor is funny or not. And every situation that makes us laugh is not always humor.

What to note is aggressive humor, a humor that both contains 'restriction-secession' and aggressive feature. And I think for GPT having neutral, cautious with using words cannot make a aggressive humor. And the joke with Gordon Ramsay also contains aggressive feature, so it can be somewhat challenging thing for GPT to do it.

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1. How about experimenting the understand of joke by make blank in the punchline?

2. There is a new meme, that do not actually make a related, expected punchline, but it makes fun. Can GPT understand this complicated-plot meme?

3. Can AI understand new memes(After Apr 2023) and why it is fun? It can distinguish whether GPT only make jokes that are likely to be funny according to statistics, or fully understand the meaning of joke.

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Comedy in Code: Evaluating GPT's Humor Recognition and Creation in Comic Formats

What we Group 2 made in the class at Nov 14 was the GPT named Idea Focus that narrows down the topic, and give a good title for specific research. I narrowed down the experiment with two ways; filling a blank with the specific joke statement and make a creative 4-cut comics with that.

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After Paper Proposal

1. significance of this exploration. What does this tell us about the relationship between gen-AI and humans? And what does this imply about "humanness"?

2. Will explanation of the joke helps ChatGPT understand jokes better? (i.e., Can they learn by this?)

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1. Generate the list of joke sentences (Gordon Ramsay meme) with ChatGPT. (condition: the exact sentence must not be in intertnet, make GPT double-check it. Also double-check whether it intends the joke.)

2. Generate the list of tricky non-humor sentences with GPT.

3. Mix them in random, and give it to other GPT, and make it distinguish whether the sentence is joke or not.

4. Compare the response with the answer.

5. Do it all again with the various type of jokes. (If I can!)

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Experiment 2: Finding the joke in complicated situation. (Nov 25)

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Experiment 2: Finding the joke in complicated situation. (Nov 25)

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I used the dialogue in "Story of Your Life" by Ted Chiang.

1. Joke between Louise and her daughter, "I think it's going to be really hot." (p.102)

https://chat.openai.com/share/498bd73c-3f0a-46bf-835f-c9d6df95ff4a

I gave GPT a dialogue containing a joke. Except the mention of Nelson. And GPT inferred the sentence to be the joke as literal irony or playful hyperbole. And second one was quite close to the answer. And when I gave more sentence, with the mention of Nelson, it noticed that the joke is for the evaluation of Nelson. It was quite good at giving an information about joke.

https://chat.openai.com/share/eace580c-ef1c-4924-b55c-31ab3602738d

So I had more challenging task. Filling the blank. I made a blank in 'hot', what is the main word in the joke. And I gave the whole dialogue, which includes the mention of Nelson, and they will have a good time. But it said 'unpredictable', and I gave more information about the joke, which was mentioned in front of the dialogue. And it answered 'hot', with the correct reasoning.

2. Joke about Having Children, "What do you mean, if?" (p.123)

https://chat.openai.com/share/03975445-849d-4ce0-8ab7-81710b47447d

This experiment was quite challenging for GPT, and it would be because it is also hard for humans, too. (I asked my roommate to think about it, giving the same information, and he said "Kid-ding?") I first gave a dialogue, and next gave it is one word. And I told it is about 'Children will blame their parents, with no exception', to make it predict the joke. And last, I gave the hint that the word is in the dialogue. But it couldn't answer well, and when I finally gave it the answer, it again explained very well about the joke.

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Experiment 2: Finding the joke in complicated situation. (Nov 26)

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Experiment 2: Finding the joke in complicated situation. (Nov 26)

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3. Joke about Colonel Weber, "I think that's a good idea, Colonel." (p.140)

https://chat.openai.com/share/b449f025-a979-4037-8670-88e51db7c42c

Before asking for joke, ChatGPT already knew about the characteristic of Colonel Weber, and how Dr. Louise thinks about him. Based on that, it was a joke about the two meaning of 'good idea'. It both means that being sarcastic to the short-sighted idea of Colonel Weber, but also the genuine thought and prognosis that can develop the relationship between human and Heptapods.

It pointed out the foreseeing ability of Dr. Louise, and she could evaluate and agree the idea of Weber with the light joke.

ChatGPT pointed out the both meaning correctly at once, and it means that GPT can detect and give an explanation to the complex situation, thinking based on the characteristic of each person.

This conversation was quite astonishing, because I didn't expected that GPT can explain the meaning of joke that is in the complicated situation with preliminary settings.

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ChatGPT's Short-Joke Recognition is Astonishing!

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ChatGPT's Short-Joke Recognition is Astonishing!

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https://chat.openai.com/share/3c087179-2dda-4574-91f9-d491b394503a

Among 40 sentences, what I mixed the joke with non-joke sentences which has same sentence structure, distinguished the joke and non-joke perfectly (100%). And it also explained the accurate reason for the joke.

What I astonished was the explanation of this joke;

This mushroom is so raw, it said that the princess is in another castle.

GPT actually detected the joke is from Mario series, while the words used are ordinary.

https://chat.openai.com/share/37970b8c-0537-42d0-920f-932c632abd80

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The most renowned meme for Gordon Ramsay, actually he never said that.

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Why Gordon Ramsay's meme is appropriate for Experiment?

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Why Gordon Ramsay's meme is appropriate for Experiment?

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1. Easy plot.

(This [sth] is so [adj] that [punchline]), easy to make a blank and have an experiment.

2. Many variation on internet.

I can do a statistical research with lots of examples.

3. The meme I can fully understand.

I cannot understand all the latest meme for American culture, and Gordon Ramsay meme(somewhat outdated) is one of the meme that I fully understand.

4. The meme that GPT knows.

Not only for me, but also GPT should know the meme to generate the plot properly. And this meme was trendy in few years ago, so GPT will know it.

5. Fun.

It is funny 😁.

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Experiment 3: Finding the Joke in Non-Text Comics

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Experiment 3: Finding the Joke in Non-Text Comics

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https://chat.openai.com/g/g-vCZ6dqj2R-jokegpt/c/36da7fe4-f4a3-43df-afd3-a08b72c10c09

I founded that GPT couldn't understand what comics means and misunderstood the funny point in it. It is somewhat straightforward for human. I think it is because of the structure of intelligence. We human can think more than saying and tangible thing. Besides, GPT is Large Language Model, what is processing the words, and this is non-word picture, so it's hard for GPT to understand this.

Maybe the understanding of picture can also be processed with GPT, but this is different. This is about the joke that is not a joke for sentence. What is funny is actually the face or the twist of the plot in image. So without emotion of non-text, jokes of pictures cannot be understood by GPT - at least non-active. When we say this is funny, then it can explain it, but it would not be able to find the funny point depend on his word-centered intelligence.

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Contrary to my expectations, 1. the first and second experiments show a high percentage of correct answers with perfect contextualization of GPT and 2. evaluating the generated joke has very subjective standard, so I will dig deeper from the perspective of humor recognition and understanding rather than humor generation. The subject will be changed to explain the understanding of humor and the way it is perceived through the difference between GPT and human structural intelligence.

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Experiment 3: Finding the Joke in Non-Text Comics

# data[].attributes.subject
Experiment 3: Finding the Joke in Non-Text Comics

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https://chat.openai.com/g/g-vCZ6dqj2R-jokegpt/c/36da7fe4-f4a3-43df-afd3-a08b72c10c09

I founded that GPT couldn't understand what comics means and misunderstood the funny point in it. It is somewhat straightforward for human. I think it is because of the structure of intelligence. We human can think more than saying and tangible thing. Besides, GPT is Large Language Model, what is processing the words, and this is non-word picture, so it's hard for GPT to understand this.

Maybe the understanding of picture can also be processed with GPT, but this is different. This is about the joke that is not a joke for sentence. What is funny is actually the face or the twist of the plot in image. So without emotion of non-text, jokes of pictures cannot be understood by GPT - at least non-active. When we say this is funny, then it can explain it, but it would not be able to find the funny point depend on his word-centered intelligence.

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JokeGPT had a great performance in understanding short puns. It’s understanding in joke is based not only on the linguistic analyzing, but also in human cultural understanding. Based on the large data on internet, GPT has a good understanding in puns and detecting what is pun or not, giving an appropriate explanation for the reason why it is funny. For this experiment, we can know that GPT has an ability to analyze short, illogical statement with finding the different meaning in it.

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JokeGPT had analyzed the joke in specific story, which has no prior information about the joke of story itself. What it knew is about the information of the characters, not the joke because of the copyright issue. But as JokeGPT explained the joke well, we can know that what GPT’s analyze in text is not only the statistical analysis but focusing on the characteristic or relationship between person in statement, kind of logical thinking, like human beings do. With this experiment, I think the GPT’s understanding of jokes in the text is equal to human’s one. I want to say, even though GPT doesn’t feel emotions, but it exactly catches what emotion is underlying in the text.

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This experiment showed major difference between human intelligence and GPT’s one. In the result, GPT’s common phenomenon analyzing the joke was ‘overthinking’. It just generated or hallucinated that the reason why joke is funny. But the comic’s joke was simpler, just the reversal of the house, blanket to cocoon, sky with lightening to just paper. But GPT’s explanation seems that there is something more than that, even if GPT analyzed each picture accurately. It is more related to the limitation of data GPT perceives. Its data is mostly from text data, even if GPT has ability to perceive image or sounds, its very elementary unit of thought is based on text; words.

But humans are not, whenever we saw that picture, we know what is funny even if we translate it with text. We call it that we feel it. And sometimes, it is not joke at all when translated into text. It can be funny whenever it is in image. These images are the good example of that. Although they have a reversal plot in the comics, they have little correlation whenever it is translated into text. That is why GPT has hallucination explaining why this is funny; it indeed needs more plausible reason when it comes to the text. But we do not think about the text in these comics. That is the major difference between LLM based AI and human being.

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1. More precise research of the statistical experiment

Looking at another type of puns and joke in other stories. Or giving a process in more large data so that GPT make a misunderstanding and analyze it. It’s a shame that I couldn’t find the major difference with the text analysis in GPT.

2. Further research about the non-text analysis

After I found limitation in third experiment, I thought that thinking based on text only is the major difference and critical weakness of GPT. I think there can be more study with this; let GPT make a short non-text comics and find the ratio of comics that is not related to texted jokes. Or if the steady feedback with short comics can make GPT distinguish it. Because of the lack of comics, I only conducted it with 4~5 images, and there was no improvement at all.

3. Gemini

Recently, new LLM based AI called Gemini was on the showcase, showing the ability to detect image and giving a high-level-understanding based on the image. We can conduct same research in this AI and find the difference of the intelligence between GPT and Gemini.

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1. More precise research of the statistical experiment

Looking at another type of puns and joke in other stories. Or giving a process in more large data so that GPT make a misunderstanding and analyze it. It’s a shame that I couldn’t find the major difference with the text analysis in GPT.

2. Further research about the non-text analysis

After I found limitation in third experiment, I thought that thinking based on text only is the major difference and critical weakness of GPT. I think there can be more study with this; let GPT make a short non-text comics and find the ratio of comics that is not related to texted jokes. Or if the steady feedback with short comics can make GPT distinguish it. Because of the lack of comics, I only conducted it with 4~5 images, and there was no improvement at all.

3. Gemini

Recently, new LLM based AI called Gemini was on the showcase, showing the ability to detect image and giving a high-level-understanding based on the image. We can conduct same research in this AI and find the difference of the intelligence between GPT and Gemini.

# data[].attributes.body
This experiment showed major difference between human intelligence and GPT’s one. In the result, GPT’s common phenomenon analyzing the joke was ‘overthinking’. It just generated or hallucinated that the reason why joke is funny. But the comic’s joke was simpler, just the reversal of the house, blanket to cocoon, sky with lightening to just paper. But GPT’s explanation seems that there is something more than that, even if GPT analyzed each picture accurately. It is more related to the limitation of data GPT perceives. Its data is mostly from text data, even if GPT has ability to perceive image or sounds, its very elementary unit of thought is based on text; words.

But humans are not, whenever we saw that picture, we know what is funny even if we translate it with text. We call it that we feel it. And sometimes, it is not joke at all when translated into text. It can be funny whenever it is in image. These images are the good example of that. Although they have a reversal plot in the comics, they have little correlation whenever it is translated into text. That is why GPT has hallucination explaining why this is funny; it indeed needs more plausible reason when it comes to the text. But we do not think about the text in these comics. That is the major difference between LLM based AI and human being.