Human Humor

Human humor research aims to understand the cognitive and linguistic mechanisms underlying humor perception and generation, with a current focus on developing computational models capable of detecting, generating, and even understanding humor in various modalities (text, image, audio-video). Researchers employ diverse approaches, including large language models (LLMs) like GPT-4 and BERT, along with techniques like feature engineering, multi-step reasoning, and multimodal fusion, to improve the accuracy and interpretability of humor-related AI systems. This field is significant for advancing our understanding of human cognition and has practical applications in areas such as improving human-computer interaction, creating more engaging AI systems, and even addressing social issues like bullying detection in educational settings.