A Survey of Automatic Humor Detection: From Theoretical Foundations to Large Language Models

Automatic humor detection, the computational task of identifying humorous content, is increasingly critical as Large Language Models (LLMs) integrate into communication platforms such as chatbots and virtual assistants. However, humor poses challenges for AI due to its reliance on context, cultural nuances, linguistic ambiguity, and multimodal cues. Current research is fragmented across humor types, languages, modalities, and evaluation benchmarks, particularly regarding modern LLM capabilities and limitations. This survey synthesizes the automatic humor detection field, tracing its evolution from foundational psychological and linguistic theories through classical machine learning, deep learning, and transformer-based LLMs. We organize and analyze computational methods, feature engineering techniques, benchmark datasets (text-only, multimodal, multilingual), and evaluation metrics. We examine LLM adaptation strategies, including fine-tuning, parameter-efficient methods (PEFT), prompt engineering, and multi-task learning, alongside multimodal and cross-lingual humor understanding developments. Our analysis reveals that while LLMs show improved performance in capturing surface humor patterns, gaps persist in pragmatic reasoning, cultural grounding, multimodal integration, and explainability. We identify open challenges, including data scarcity, evaluation inconsistencies, the humor–offensiveness boundary, and the need for culturally aware and interpretable models. By consolidating progress and pinpointing limitations, this survey guides future interdisciplinary research toward developing socially intelligent AI systems capable of understanding humor.