# Sarc7: Evaluating Sarcasm Detection and Generation with Seven Types and Emotion-Informed Techniques

arXiv: https://arxiv.org/abs/2506.00658 · HTML: https://mlsanity.com/paper/2506.00658/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 31 May 2025.

## Builds on (most cited first, 7 of 7)

- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)](https://mlsanity.com/paper/1906.01815/) (2019)
- [SarcasmBench: Towards Evaluating Large Language Models on Sarcasm Understanding](https://mlsanity.com/paper/2408.11319/) (2024)
- [Pragmatic Metacognitive Prompting Improves LLM Performance on Sarcasm Detection](https://mlsanity.com/paper/2412.04509/) (2024)
- [Is Sarcasm Detection A Step-by-Step Reasoning Process in Large Language Models?](https://mlsanity.com/paper/2407.12725/) (2024)
- [On Sarcasm Detection with OpenAI GPT-based Models](https://mlsanity.com/paper/2312.04642/) (2023)

## Built on by (most cited first, 1 of 1)

- [Benchmarking the Benchmarks: Testing the Predictive Validity of Commonsense Benchmarks](https://mlsanity.com/paper/2608.03340/) (2026)

## Abstract (arXiv)

Sarcasm is a form of humor where expressions convey meanings opposite to their literal interpretations. Classifying and generating sarcasm using large language models is vital for interpreting human communication. Sarcasm poses challenges for computational models, due to its nuanced nature. We introduce Sarc7, a benchmark that classifies 7 types of sarcasm: self-deprecating, brooding, deadpan, polite, obnoxious, raging, and manic by annotating entries of the MUStARD dataset. Classification was evaluated using zero-shot, few-shot, chain-of-thought (CoT), and a novel emotion-based prompting technique. We propose an emotion-based generation method developed by identifying key components of sarcasm-incongruity, shock value, and context dependency. Our classification experiments show that Gemini 2.5, using emotion-based prompting, outperforms other setups with an F1 score of 0.3664. Human evaluators preferred our emotion-based prompting, with 38.46% more successful generations than zero-shot prompting.