Which LLMs Get the Joke? Probing Non-STEM Reasoning Abilities with a New Benchmark

ACL ARR 2025 May Submission5523 Authors

Abstract:

We present HumorBench, a benchmark designed to evaluate large language models’ (LLMs) ability to reason about and explain sophisticated humor in cartoon captions. As reasoning models increasingly saturate existing benchmarks in mathematics and science, novel and challenging evaluations of model intelligence beyond STEM domains are essential. Reasoning is fundamentally involved in text-based humor comprehension, requiring the identification of connections between concepts in cartoons/captions and external cultural references, wordplays, and other mechanisms. HumorBench includes approximately 300 unique cartoon-caption pairs from the New Yorker Caption Contest and Cartoonstock.com, with expert-annotated evaluation rubrics identifying essential joke elements. LLMs are evaluated based on their explanations towards the humor and abilities in identifying the joke elements. To perform well on this task, models must form and test hypotheses about associations between concepts, potentially backtracking from initial interpretations to arrive at the most plausible explanation. Our extensive benchmarking of current SOTA models reveals three key insights:
(1) LLM progress on STEM reasoning transfers effectively to humor comprehension;
(2) models trained exclusively on STEM reasoning data still perform well on HumorBench, demonstrating strong transferability of reasoning abilities; and
(3) test-time scaling by increasing thinking token budgets yields mixed results across different models in humor reasoning.

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: Resources and Evaluation, Humor of LLMs, Benchmark, Reasoning

Contribution Types: Data resources

Languages Studied: English

Keywords: Humor of LLMs, Benchmark, Reasoning

Submission Number: 5523