# Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest

arXiv: https://arxiv.org/abs/2209.06293 · HTML: https://mlsanity.com/paper/2209.06293/

## Key facts

- Cited by 98 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 94 references that are in the map.
- First posted on arXiv 13 Sep 2022.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework](https://mlsanity.com/paper/2202.03052/) (2022)
- [Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language](https://mlsanity.com/paper/2204.00598/) (2022)
- [Hypothesis Only Baselines in Natural Language Inference](https://mlsanity.com/paper/1805.01042/) (2018)
- [Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)](https://mlsanity.com/paper/1906.01815/) (2019)
- [UR-FUNNY: A Multimodal Language Dataset for Understanding Humor](https://mlsanity.com/paper/1904.06618/) (2019)
- [Reframing Human-AI Collaboration for Generating Free-Text Explanations](https://mlsanity.com/paper/2112.08674/) (2021)
- [Few-Shot Self-Rationalization with Natural Language Prompts](https://mlsanity.com/paper/2111.08284/) (2021)
- [Does my multimodal model learn cross-modal interactions? It's harder to tell than you might think!](https://mlsanity.com/paper/2010.06572/) (2020)
- [On the Diversity and Limits of Human Explanations](https://mlsanity.com/paper/2106.11988/) (2021)
- [How Reasonable are Common-Sense Reasoning Tasks: A Case-Study on the Winograd Schema Challenge and SWAG](https://mlsanity.com/paper/1811.01778/) (2018)
- [Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contest](https://mlsanity.com/paper/1506.08126/) (2015)
- [We Are Humor Beings: Understanding and Predicting Visual Humor](https://mlsanity.com/paper/1512.04407/) (2015)
- [Punny Captions: Witty Wordplay in Image Descriptions](https://mlsanity.com/paper/1704.08224/) (2017)
- [Neural Joking Machine : Humorous image captioning](https://mlsanity.com/paper/1805.11850/) (2018)

## Built on by (most cited first, 30 of 98)

- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?](https://mlsanity.com/paper/2407.01284/) (2024)
- [BIG-Bench Extra Hard](https://mlsanity.com/paper/2502.19187/) (2025)
- [VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use](https://mlsanity.com/paper/2308.06595/) (2023)
- [Language Models Still Struggle to Zero-shot Reason about Time Series](https://mlsanity.com/paper/2404.11757/) (2024)
- [Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images](https://mlsanity.com/paper/2303.07274/) (2023)
- [Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment](https://mlsanity.com/paper/2311.08596/) (2023)
- [Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation](https://mlsanity.com/paper/2312.02439/) (2023)
- [MemeCap: A Dataset for Captioning and Interpreting Memes](https://mlsanity.com/paper/2305.13703/) (2023)
- [Neuro-Symbolic AI in 2024: A Systematic Review](https://mlsanity.com/paper/2501.05435/) (2025)
- [FunQA: Towards Surprising Video Comprehension](https://mlsanity.com/paper/2306.14899/) (2023)
- [MacGyver: Are Large Language Models Creative Problem Solvers?](https://mlsanity.com/paper/2311.09682/) (2023)
- [Probing the Creativity of Large Language Models: Can models produce divergent semantic association?](https://mlsanity.com/paper/2310.11158/) (2023)
- [SMILE: Multimodal Dataset for Understanding Laughter in Video with Language Models](https://mlsanity.com/paper/2312.09818/) (2023)
- [Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art](https://mlsanity.com/paper/2406.03930/) (2024)
- [When LLMs Meet Cunning Texts: A Fallacy Understanding Benchmark for Large Language Models](https://mlsanity.com/paper/2402.11100/) (2024)
- [Multimodal Learning Without Labeled Multimodal Data: Guarantees and Applications](https://mlsanity.com/paper/2306.04539/) (2023)
- [MMoE: Enhancing Multimodal Models with Mixtures of Multimodal Interaction Experts](https://mlsanity.com/paper/2311.09580/) (2023)
- ["A good pun is its own reword": Can Large Language Models Understand Puns?](https://mlsanity.com/paper/2404.13599/) (2024)
- [HEMM: Holistic Evaluation of Multimodal Foundation Models](https://mlsanity.com/paper/2407.03418/) (2024)
- [Understanding Figurative Meaning through Explainable Visual Entailment](https://mlsanity.com/paper/2405.01474/) (2024)
- [Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for Cartoon Captioning](https://mlsanity.com/paper/2406.10522/) (2024)
- [Enhancing Advanced Visual Reasoning Ability of Large Language Models](https://mlsanity.com/paper/2409.13980/) (2024)
- [Can Large Multimodal Models Uncover Deep Semantics Behind Images?](https://mlsanity.com/paper/2402.11281/) (2024)
- [Do Large Language Models Understand Conversational Implicature -- A case study with a chinese sitcom](https://mlsanity.com/paper/2404.19509/) (2024)
- [Can MLLMs Understand the Deep Implication Behind Chinese Images?](https://mlsanity.com/paper/2410.13854/) (2024)
- [What Are We Measuring When We Evaluate Large Vision-Language Models? An Analysis of Latent Factors and Biases](https://mlsanity.com/paper/2404.02415/) (2024)
- [Crowd Score: A Method for the Evaluation of Jokes using Large Language Model AI Voters as Judges](https://mlsanity.com/paper/2212.11214/) (2022)
- [Shallow Synthesis of Knowledge in GPT-Generated Texts: A Case Study in Automatic Related Work Composition](https://mlsanity.com/paper/2402.12255/) (2024)
- [VIVA: A Benchmark for Vision-Grounded Decision-Making with Human Values](https://mlsanity.com/paper/2407.03000/) (2024)

## Abstract (arXiv)

Large neural networks can now generate jokes, but do they really "understand" humor? We challenge AI models with three tasks derived from the New Yorker Cartoon Caption Contest: matching a joke to a cartoon, identifying a winning caption, and explaining why a winning caption is funny. These tasks encapsulate progressively more sophisticated aspects of "understanding" a cartoon; key elements are the complex, often surprising relationships between images and captions and the frequent inclusion of indirect and playful allusions to human experience and culture. We investigate both multimodal and language-only models: the former are challenged with the cartoon images directly, while the latter are given multifaceted descriptions of the visual scene to simulate human-level visual understanding. We find that both types of models struggle at all three tasks. For example, our best multimodal models fall 30 accuracy points behind human performance on the matching task, and, even when provided ground-truth visual scene descriptors, human-authored explanations are preferred head-to-head over the best machine-authored ones (few-shot GPT-4) in more than 2/3 of cases. We release models, code, leaderboard, and corpus, which includes newly-gathered annotations describing the image's locations/entities, what's unusual in the scene, and an explanation of the joke.