How do you teach computers humor + Text Generators as Creative Partners (May 2023)

1. How do you teach computers humor? Thomas Winters PhD Student at KU Leuven & FWO Fellow @thomas_wint thomaswinters.be Slides: thomaswinters.be/talk/2023cc

2. One you brush and rake, What is the difference between leaves and a car? the other you rush and brake.

3. We asked the Belgian prime minister for comment, but he was too busy stockpiling firewood. Belgian prime minister says the next five winters will be difficult due less energy supply.

5. Can a machine have a sense of humor?

6. Computer + humor = paradox?

7. Humor = intrinsically human

9. Thomas Winters • PhD student since 2018 • computational humor • creative AI • language models • Part of Machine Learning Lab in DTAI group @ KU Leuven

10. Why do computers need humor?

11. Lowering humor learning curve for humans

12. Humor co-creation Grammarly / Clippy but for jokes

13. My ex-wife still misses me, but her aim is steadily improving. "I told my dog to sit, but he just lay down and refused to move." Humor co-creation Adding more specifics can help funniness, for example “plopped down and staged a protest”

14. Future conversational agents need to know when you’re joking People often treat virtual assistants like humans (“please”, “thank you”, “sorry”) [1] [1] https://www.thinkwithgoogle.com/consumer-insights/voice-assistance-consumer-experience/ [2] Laughter’s Influence on the Intimacy of Self-Disclosure, Gray, A.W., Parkinson, B. & Dunbar, R.I. Hum Nat (2015) 26: 28. Humor and creativity in language is important between friends [2]

15. Casual Creation: it’s fun to create these little bots! https://thomaswinters.be/mopjesbot Winters, T. (2019). Generating Dutch Punning Riddles about Current Affairs

16. Studying & understanding humor We do not fully know how humor works Artificial Intelligence can shed a light! Humour

18. Part 1: Humor theory

19. Purpose of Humor = Sign of Intelligence? huh? aha! that’s funny Brain rewards noticing incongruities and successfully resolving them + linguistic skills + hard to fake display of personal values = Evolutionary advantage! h

20. How does humor work?

21. E.B. White Analyzing humor is like dissecting a frog. You understand it better, but the frog dies in the process

22. Incongruity-Resolution Theory Based on: Ritchie, G. (1999). Developing the incongruity-resolution theory. Obvious Interpretation Hidden Interpretation Two fish are in a tank. Says one to the other: “Do you know how to drive this thing?” Setup Punchline

23. Humor breaks the predicted world model Everyone loved jumping from the tree into the swimming pool, so we decided to put in a little water ? ? ? ?

24. Human-focused definition! Machine should not only spot two mental images Obvious Interpretation Hidden Interpretation But also that it is not too hard or too easy for a human!

25. Humor = AI-complete? ~ as hard as making computers as “intelligent” as people

26. Part 2: Computers can follow humor instructions

27. Templates, Schemas, Grammars Hard-coding a sense of humor

28. JAPE: Templates & Schemas What’s <CharacteristicNP> and <Characteristic1> ? A <Word1> <Word2>. Noun Phrase Word1 Word2 Homophone1 Characteristic1 CharacteristicNP What’s green and bounces? A spring cabbage. spring (season) to bounce spring (elastic body) cabbage green spring cabbage Binsted, K., & Ritchie, G. (1994). An implemented model of punning riddles

29. Unsupervised Analogy Generator I like my <X> like I like my <Y>: <Z> I like my coffee like I like my war: cold. Petrović, S., & Matthews, D. (2013). Unsupervised joke generation from big data cold war coffee Z Y X Maximise adjective dissimilarity Maximise co-occurrence Maximise co-occurrence Maximise # definitions Minimize commonness

30. Generative grammars S  <Greeting>, <Location> Greeting  “Hello” | “Hi” Location  “world” | “universe” Generates: “Hello, world”, “Hi, world”, “Hello, universe” & “Hi, universe”

31. Templates + Grammar + Functions = Babbly Babbly: my programming language for text generation with grammars import firstname.words food = pasta|pizza|sushi main = { 3: <firstname> loves <food.uppercase>! 1: <firstname> (quite|reasonably|fairly) likes <food>. Oo{1,3}h, I hope they join! 1: <firstname:protagonist> is not (quite){.5} fond of <food:>. <firstname:protagonist> will thus not go to the <food:> (restaurant|place). } Generates sentences like: • Lieven loves PIZZA! • Thomas is not quite fond of sushi. Thomas will thus not go to a sushi place. Available on github.com/twinters/babbly Other popular grammar modelling alternative: Tracery

32. Winters, T. (2019). Modelling Mutually Interactive Fictional Character Conversational Agents Generative grammar for simulating Samson

33. Talk Generator Generates nonsense PowerPoints about any given topic for presenters to improvise on Winters T., Mathewson K. (2019). Automatically Generating Engaging Presentation Slide Decks Try it yourself: talkgenerator.com

34. Slide generator

35. Slide topic generation Slide 1 cat Slide 2 Slide 3 Slide 4 Slide 5 Slide 6 cat Slide 7 Slide 8 Slide 9 cat Slide 10 cat wash paws corner mouse pet mouse ? ? ? ? ? ? rug found in persia ConceptNet Common Sense Knowledge Graph

36. Talkgenerator.com Internally uses lots of handwritten grammars, combined with search engines and knowledge graphs Winters T., Mathewson K. (2019). Automatically Generating Engaging Presentation Slide Decks

38. Learning joke generator structures automatically

39. Dynamic Templates “Are there also atheists that don't believe in atheism?” “They see the fact that the former God is not trying to deny this newly acquired insight as proof of them being right. Even with the Church, things are not going well. Norse popes.” “Are there also popes that don't believe in God?” Winters, T. (2019). Generating philosophical statements using interpolated markov models and dynamic templates. Used in @TorfsBot Replace some matching words with matching POS tag with lowest frequencies

40. GITTA: Template Trees for extracting templates 1. Join closest strings 2. Merge similar template slots 3. Iteratively simplify until convergence A: I like my <B> and my <B> | <G> the <B> is <F> B: chicken | cat | dog F: walking | jumping G: Alice | Bob | Cathy Winters, T., & De Raedt, L. (2020). Discovering Textual Structures: Generative Grammar Induction using Template Trees. Input

41. Generalising Schemas to Enable Machine Learning Constraint-based Schemas Hard to tweak to preference Metrical Schema Allows for learning in aggregator component Header Keywords Template Features Aggregator Values Generator Humour Winters, T., Nys, V., & De Schreye, D. (2019). Towards a general framework for humor generation from rated examples. Noun Phrase Word1 Word2 Homophone1 Characteristic1 CharacteristicNP

42. X Y Z Score FreqX FreqY FreqZ FreqXY … men grave nameless ? 9 876 543 4 050 904 4884757 0 … men country cold ? 9 876 543 9 211 826 2 010 173 0 … men turkey roast ? 9 876 543 998 669 5 284 057 0 …. men rain gentle ? 9 876 543 9 876 543 845 321 0 … men laugh silly ? 9 876 543 954 569 123 456 0 …. Human Evaluation Template Extractor Template Store Values Generator Metric-based Rater Classifier/ Regression Input Jokes Rated Jokes Set of rated template values per template Rated template values with feature values (training data) Input topic Word + Template Generated template values Template values + features Jokes about seed 1. Training 2. Generating (Apply Template) Templates Filtered template values set a) I like my relations like I like my source, open. b) I like my coffee like I like my war, cold. c) The sailor bears a stress. Pier pressure. d) I like my girlfriend like I like my estate: real. e) The lord cultivates a region. A baron land. Joke Rater 1 Rater 2 Rater 3 a 5 4 5 b 4 4 3 c 5 3 4 d 2 1 3 e 3 2 2 Template 1: “I like my X like I like my Y, Z.” relations, source, open | [5,4,5] coffee, war, cold | [4,4,3] girlfriend, estate, real | [2,1,3] Template 2: ``The A B a C. D.‘’ sailor, bears, stress, Pier pressure | [5,3,4] lord, cultivates, region, A baron land | [3,2,2] X Y Z Score FreqX FreqY FreqZ FreqXY … relations source open [5,4,5] 3 831 210 4 050 904 4884757 0 … coffee war cold [4,4,3] 784 065 9 211 826 2 010 173 0 … girlfriend estate real [2,1,3] 75 392 998 669 5 284 057 0 …. “Men” “Men” + “I like my X like I like my Y, Z.” a) men, grave, nameless b) men, country, cold c) men, turkey, roast d) men, rain, gentle e) men, laugh, silly men, grave, nameless => 5 men, country, cold => 1 men, turkey, roast =>3 men, rain, gentle =>4 men, laugh, silly => 1 I like my men like I like my grave: nameless I like my men like I like my rain: gentle GOOFER: Generalised framework Winters, T., Nys, V., & De Schreye, D. (2019). Towards a general framework for humor generation from rated examples.

43. GAG: implements GOOFER framework 11.41% 22.61% 21.08% GAG: Classifier Human-created on JokeJudger Human-created single-word jokes 4+ RATINGS FREQUENCY I like my coffee like I like my sleep: strong. I like my men like I like my graves: nameless. I like my women like I like my tests: independent. Winters, T., Nys, V., & De Schreye, D. (2018). Automatic Joke Generation: Learning Humor from Examples

45. Humor Detection

46. Early Humor Detector • Designed humor features e.g. alliteration, antonym, adult slang... • Used Naive Bayes & Support Vector Machines • Task: One-liners vs news, neutral corpus & proverbs Mihalcea, R., & Strapparava, C. (2005). Making computers laugh: Investigations in automatic humor recognition.

47. But is this a good dataset? News & proverbs have completely different types of words than jokes!  Looking at word frequencies is often already “enough”! Is this really humor detection?

48. Jokes are fragile! Two fish are in a tank. Says one to the other: “Do you know how to drive this thing?” men bar Generate non-jokes by replacing keywords from other jokes! Word-based features won’t work anymore! Winters T., Delobelle P. (2020). Dutch humor detection by generating negative examples. BNAIC/Benelearn2020

49. Examples of generated Dutch non-jokes Het is groen en het is een mummie? Kermit de Waterkant Wat is het toppunt van principe? 1) Wachten totdat een Nederlander gaat twijfelen 2) Een Zuster met een autoladder 3) Een brandwacht brandmeester met een brandmeester van 9 maanden “Ober, kunt u die schrik uit mijn politieman halen? Want ik eet liever alleen.” "Mijn hond is heel vreselijk: Hij schreeuwt mij iedere zus de broer.“ "Maar dat is toch niet zo heel vreselijk?“ "Jawel, want ik heb geen rapport!" Wat staat er midden in het bos? De kapper. Er loopt een super vriendelijk blondje langs een armband. Last er een toonbank: “zo, waargaan die mooie mannen heen?” Blondje: “naar de barkeeper als er niets tussen komt…” Hoe heet de vrouw van Sinterklaas? Keukentafel. "Twee tanden zwemmen in de zee en ze zien een stamgast op een stamgast. De ene raad zegt tegen de andere raad: 'Hé kijk! Ons eten op een bord!'"

50. 51% 60% 50% 94% 94% 47% 94% 94% 47% 99% 96% 89% Jokes vs News Jokes vs Proverbs Jokes vs Generated Jokes Binary classification of jokes versus texts from other domains Naive Bayes LSTM CNN RobBERT Much more challenging dataset! More truthful humor detection? Winters T., Delobelle P. (2020). Dutch humor detection by generating negative examples. BNAIC/Benelearn2020

52. Part 3: “Large language models are all you need”

53. Transformer models Large language models, pretrained on large corpora Outperforming previous neural architectures on most language tasks GPT Completes any textual prompt BERT Classifies any text sequence / token Brown, Tom B., et al. "Language models are few-shot learners." Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding

54. ≈ autocomplete on steroids

55. Getting dialogue out of GPT-3 Input - Fellow ants, we have great news. Our new ant queen is born! - Hello my loyal ants, I promise I will rule our nest with an iron claw! - All hail our new ant queen! —————— - I am the new ant queen, and I will make our colony the strongest in the world! —————— - It's time for us to expand our nest! - Our nest is under attack! “-” trick for getting it to generate dialogue Outputs

56. Improbotics A.L.Ex the robot plays improv scenes with improv actors “Listens” to scene and formulates own responses with GPT-3 Later in show: gains human body. AI whispers through ear piece what actor should say. End: everyone has ear piece  Turing test!

57. Improbotics interface Operator types what happens and selects responses

58. GPT-2 / GPT-3 joke examples GPT-2 can mimic joke style, but usually very absurd https://www.gwern.net/GPT-3 https://towardsdatascience.com/teaching-gpt-2-a-sense-of-humor-fine-tuning-large-transformer-models-on-a-single-gpu-in-pytorch-59e8cec40912 What did one plate say to the other plate? Dip me! What did the chicken say after he got hit by a bus? "I'm gonna be fine!" A woman walks into the bar......she says to the bartender "I want a double entendre" and the bartender says "No, we don't serve that"

59. ChatGPT generated “jokes”

60. Reminiscent of how children write jokes

61. Problem 1: Predicting most likely Language models predict most logical next words Humor breaks what is expected! Great at mimicking patterns, But usually fails to create punchline

62. Problem 2: Tokenization

64. The art of prompt engineering

65. The art of prompt engineering

66. Humor prompt that allows “draft paper”

67. Explains it’s “thinking process”

68. Can computers have a sense of humor? Computers can follow specific humor instructions Humor is intrinsically human Large language models guide the revolution

69. but: AI-complete problem! Need to process two mental images before fully understanding the joke

70. How do you teach computers humor? Thomas Winters PhD Student at KU Leuven & FWO Fellow @thomas_wint thomaswinters.be Slides: thomaswinters.be/talk/2023cc

71. (break)

72. Textgenerators as creative partner for humor Thomas Winters PhD Student at KU Leuven & FWO Fellow @thomas_wint thomaswinters.be Where does GPT come from, and how to optimally use it? Slides: thomaswinters.be/2023humor

73. Who has used a GPT system before?

74. Who has used GPT to solve an actual need (work, study...)?

75. Part 1 Textgeneration History Overview on the ~4 GPT waves

76. How do autoregressive language models work? Demo 1. Open your smartphone keyboard 2. Press on any autocomplete word 3. Press 10-20 times 4. You’ve just generated a sentence using an AI trained to sound similar to you! Autocomplete counted how often you used certain words after other words in previously typed texts I have been trying to get hold of my client since the last few days

77. Example: TorfsBot 1. Counted in all tweets & columns of Rik Torfs how often one word follows others 2. Then starts with two random words and randomly select words Rik used after the last couple of words “gevolgd door” 4: een 2: zijn 1: iemand 1: acht Beste,

78. ≈ autocomplete on steroids Autoregressive models like GPT Probability of next token given previous tokens

79. GPT training Whole textual internet Training process of several million $ Beste publieke taalmodel

80. First GPT-3 (2020) Can do task given few examples = Pretrain & Few-shot Prompt Keeps babbling after answer e.g., “hallucinating” non-existing comments 4000 tokens input-output limit

81. GPT update cycle

82. Instruct GPT-3 Follows instructions = Pretrain & Zero-shot Prompt Shuts up when completed Less toxicity & hallucinations

83. RLHF (Reinforcement Learning from Human Feedback)

84. ChatGPT Uses RLHF-based GPT-3 Finetuned on chats Fastest growing platform ever

85. https://platform.openai.com/tokenizer

86. Main strengths of GPT Copy writing  Copy editing Amazing at paraphrasing to any style & format

87. Even to extremer styles

89. Prompt Engineering Past: How to implement AI? Now: How to optimally ask to “general” AI? Part 2

90. Optimally choosing words for AI “Voodoo magic” But also empirically proven tricks Often intuitive why it works What is Prompt Engineering?

91. 6 tricks for enhancing any creative task prompt

92. Base: simple write instruction prompt

93. Trick 1: Give handful of examples (“Few-shot”)

94. Trick 2: Explain role (“role-prompting”) Tell ChatGPT to pretend to be an expert. For example: You are an expert X with proven track record on X’. I am looking to Y. Your task is Z. My first command is … “localizes” task in model More inspirations https://github.com/f/awesome- chatgpt-prompts

95. Let’s try role-prompting & few-shot! 1. Pick creative task (preferably short text: easier to quickly see progress!) For example • Tweet from particular user • Writing short poem / joke / satire • Planning an event • Summarizing article into catchy headline • ... 2. Go to chat.openai.com of platform.openai.com/playground 3. Write simple write prompt & check result 4. Improve with role-prompting & check result • You are an expert X with proven track record on X’. I am looking to Y. Your task is Z. My first command is … • Pretend to be X. You are not an AI model, you are X. You have opinions about everything. You must follow the following rules in every reply: ... • ... 5. Add examples (few-shot) & check result Slides: thomaswinters.be/2023humor

96. GPT is bad at math?

97. Trick 3: Ask for reasoning steps (“Zero-shot chain-of-thought”)

98. Works for any task! Best in format “Q: …. A: Let’s think step by step.”

101. Any “chain of thought”-provoking prompt helps!

102. Trick 4: Make it do multiple explicit steps

105. Trick 5: Reasoning process examples (“Few-shot chain-of-thought”) Instead of giving examples like Input: … Output: … Add explanation BEFORE output (so it can be used when calculating the output): Input: … Reasoning: … Output: …

106. I decided to sell my Hoover. Well… It was just collecting dust https://www.yout ube.com/watch?v= HCn9lkazxjk

107. Trick 5: Reasoning process examples (“Few-shot chain-of-thought”) Input: vacuum cleaner Output: I decided to sell my Hoover… Well, it was just collecting dust.  Input: vacuum cleaner Proverb: collecting dust Reasoning: Let's think step by step. Collecting dust means you don't use something. Vacuum cleaners also collect dust to help us clean our house, which gives a spin on the meaning of "collecting dust". Punchline: my vacuum cleaner is collecting dust

108. Trick 5: Reasoning process examples (“Few-shot chain-of-thought”)

111. Trick 6: Force ChatGPT to ask questions ChatGPT is trained to answer immediately (RLHF)  Force it to ask questions! Best easy prompt is: You are an expert X with proven track record on X’. I am looking to Y. Your task is Z, while constantly asking questions to better grasp what I’m looking for. Do you understand what I’m saying? https://www.youtube.com/watch?v=Qm A7S2iGBjk

112. Let’s add Chain of Thought! 1. Take your best prompt from previous exercise 2. Improve your prompt by applying a chain-of-thought trick 1. Zero-shot Chain-of-thought (“Q: ... A: Let’s think step by step”) 2. Multistep (specify explicit thinking steps) 3. Few show Chain-of-thought (Add reasoning right before your example answers) Slides: thomaswinters.be/2023humor

113. Textgenerators as creative partner for humor Thomas Winters PhD Student at KU Leuven & FWO Fellow @thomas_wint thomaswinters.be Where does GPT come from, and how to optimally use it? Slides: thomaswinters.be/2023humor

How do you teach computers humor + Text Generators as Creative Partners (May 2023)

How do you teach computers humor + Text Generators as Creative Partners (May 2023)

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