# Evaluating Multimodal Large Language Models on Spoken Sarcasm Understanding

arXiv: https://arxiv.org/abs/2509.15476 · HTML: https://mlsanity.com/paper/2509.15476/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 16 of its 24 references that are in the map.
- First posted on arXiv 18 Sep 2025.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [Qwen2.5-Omni Technical Report](https://mlsanity.com/paper/2503.20215/) (2025)
- [Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models](https://mlsanity.com/paper/2311.07919/) (2023)
- [Use What You Have: Video Retrieval Using Representations From Collaborative Experts](https://mlsanity.com/paper/1907.13487/) (2019)
- [Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)](https://mlsanity.com/paper/1906.01815/) (2019)
- [GOAT-Bench: Safety Insights to Large Multimodal Models through Meme-Based Social Abuse](https://mlsanity.com/paper/2401.01523/) (2024)
- [Detecting Sarcasm in Multimodal Social Platforms](https://mlsanity.com/paper/1608.02289/) (2016)
- [Multimodal Learning using Optimal Transport for Sarcasm and Humor Detection](https://mlsanity.com/paper/2110.10949/) (2021)
- [MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System](https://mlsanity.com/paper/2307.07135/) (2023)
- [A Multimodal Corpus for Emotion Recognition in Sarcasm](https://mlsanity.com/paper/2206.02119/) (2022)
- [MM-BigBench: Evaluating Multimodal Models on Multimodal Content Comprehension Tasks](https://mlsanity.com/paper/2310.09036/) (2023)

## Built on by (most cited first, 2 of 2)

- [Modeling Sarcastic Speech: Semantic and Prosodic Cues in a Speech Synthesis Framework](https://mlsanity.com/paper/2510.07096/) (2025)
- [When Misinformation Speaks and Converses: Rethinking Fact-Checking in Audio Platforms](https://mlsanity.com/paper/2604.16767/) (2026)

## Abstract (arXiv)

Sarcasm detection remains a challenge in natural language understanding, as sarcastic intent often relies on subtle cross-modal cues spanning text, speech, and vision. While prior work has primarily focused on textual or visual-textual sarcasm, comprehensive audio-visual-textual sarcasm understanding remains underexplored. In this paper, we systematically evaluate large language models (LLMs) and multimodal LLMs for sarcasm detection on English (MUStARD++) and Chinese (MCSD 1.0) in zero-shot, few-shot, and LoRA fine-tuning settings. In addition to direct classification, we explore models as feature encoders, integrating their representations through a collaborative gating fusion module. Experimental results show that audio-based models achieve the strongest unimodal performance, while text-audio and audio-vision combinations outperform unimodal and trimodal models. Furthermore, MLLMs such as Qwen-Omni show competitive zero-shot and fine-tuned performance. Our findings highlight the potential of MLLMs for cross-lingual, audio-visual-textual sarcasm understanding.