Evaluating Multimodal Large Language Models on Spoken Sarcasm Detection

Sarcasm detection remains challenging because sarcastic intent often depends on information across text, speech, and vision. We systematically evaluate large language models (LLMs) and multimodal LLMs (MLLMs) for sarcasm detection on English (MUStARD++) and Chinese (MCSD), considering both feature-extraction settings and direct classification. In the feature-extraction settings, audio provides the strongest unimodal performance on both datasets, while multimodal fusion shows dataset-dependent benefits. In direct classification, Qwen2.5-Omni achieves the strongest zero-shot performance among the evaluated models, with further substantial gains after fine-tuning. Error analysis further shows that additional modalities can both correct and introduce errors, highlighting the need for selective multimodal integration.