Seeing Sarcasm Through Different Eyes: Analyzing Multimodal Sarcasm Perception in Large Vision-Language Models
Introduced a multimodal sarcasm analysis framework, evaluating 12 LVLMs over 2409 samples.
Key Findings
Methodology
The study proposed a new analytical framework using systematically designed prompts on existing multimodal sarcasm datasets. Evaluated 12 state-of-the-art LVLMs over 2409 samples, focusing on confidence levels, alignment with dataset labels, and recognition of ambiguous 'neutral' cases.
Key Results
- Result 1: LVLMs showed higher consistency in classification tasks but diverged significantly in interpretive reasoning.
- Result 2: Smaller models were more sensitive to prompt variations, while larger models showed stable performance.
- Result 3: Validated findings on a diverse 100-sample mini-benchmark.
Significance
The study reveals LVLMs' potential for rich, human-like multimodal sarcasm comprehension while challenging current approaches that rely heavily on binary labeling paradigms. Advocates for uncertainty-aware, multi-perspective modeling methods to better capture sarcasm's subjectivity.
Technical Contribution
Proposed a new evaluation framework comprising four tasks, enabling a multi-perspective assessment of LVLMs for multimodal sarcasm. Demonstrated significant divergence in sarcasm assessments across different LVLMs, exposing pitfalls of over-reliance on binary ground truth annotations.
Novelty
First to propose a multi-task evaluation of LVLMs' multimodal sarcasm understanding capabilities, providing a more interpretive and subjective analysis perspective.
Limitations
- Limitation 1: Models show significant ambiguity in recognizing neutral samples.
- Limitation 2: Smaller models are sensitive to prompt variations, affecting consistency.
Future Work
Future directions include developing more sophisticated multimodal models to better handle sarcasm's subjectivity and uncertainty.
AI Executive Summary
With the advent of large vision-language models (LVLMs) demonstrating increasingly human-like abilities, researchers have proposed a new analytical framework to evaluate these models' performance in multimodal sarcasm comprehension. Using systematically designed prompts, the study evaluated 12 state-of-the-art LVLMs over 2409 samples, focusing on confidence levels, alignment with dataset labels, and recognition of ambiguous 'neutral' cases. Results showed LVLMs exhibited higher consistency in classification tasks but diverged significantly in interpretive reasoning. Smaller models were more sensitive to prompt variations, while larger models showed stable performance. Findings were validated on a diverse 100-sample mini-benchmark. The study reveals LVLMs' potential for rich, human-like multimodal sarcasm comprehension while challenging current approaches that rely heavily on binary labeling paradigms. Advocates for uncertainty-aware, multi-perspective modeling methods to better capture sarcasm's subjectivity.
Deep Analysis
Background
Multimodal sarcasm detection is a complex task involving the fusion of textual and visual cues. Existing research primarily focuses on sarcasm detection, explanation, and localization tasks, relying on specialized datasets like MMSD and MMSD2.0.
Core Problem
The subjectivity of sarcasm poses a persistent challenge in computational perception, especially in multimodal contexts. Traditional methods rely on binary labels, failing to capture the diversity of sarcasm.
Innovation
Proposed a new evaluation framework comprising four tasks: Binary Sarcasm Classification, Ternary Sarcasm Classification, Sarcasm-Centric Scoring, and Literal-Centric Scoring. Each task is evaluated through different prompt variants.
Methodology
- �� Binary Sarcasm Classification: Models classify samples as sarcastic or non-sarcastic with rationale.
- �� Ternary Sarcasm Classification: Adds a neutral category, reflecting samples' potential to be seen as sarcastic or non-sarcastic.
- �� Sarcasm-Centric Scoring: Models interpret samples from a sarcastic lens.
- �� Literal-Centric Scoring: Models interpret samples from a literal perspective.
Experiments
Experiments were conducted using the MMSD2.0 dataset, evaluating 12 LVLMs over 2409 samples. Three prompt variants were designed to ensure robustness of conclusions.
Results
Results showed LVLMs exhibited higher consistency in classification tasks but diverged significantly in interpretive reasoning. Smaller models were more sensitive to prompt variations, while larger models showed stable performance.
Applications
Multimodal sarcasm detection can be applied in social media analysis, sentiment analysis, and opinion mining, aiding in understanding complex user expressions.
Limitations & Outlook
Models show significant ambiguity in recognizing neutral samples, and smaller models are sensitive to prompt variations, affecting consistency. Future research needs to develop more sophisticated models to handle sarcasm's subjectivity.
Plain Language Accessible to non-experts
Imagine a kitchen where a chef needs to judge the taste of a dish based on different ingredient combinations. Similarly, models need to combine text and images to judge sarcasm. Just like a chef uses experience to judge taste, models use prompts to judge sarcasm.
ELI14 Explained like you're 14
Hey, pals! Imagine you're playing a game, and sometimes you use sarcasm to express frustration, like saying 'Great, my teammate disconnected again!' This paper is like a super-smart game assistant that can understand your sarcastic remarks and help analyze what you mean. Isn't that cool?
Glossary
Multimodal Sarcasm Detection
Technology that combines text and images to identify sarcasm.
Used to analyze complex expressions on social media.
LVLM
Large vision-language models capable of handling multimodal content.
Used for multimodal sarcasm detection.
Prompt Engineering
Technique for designing prompts to guide model outputs.
Used to evaluate models' sarcasm understanding capabilities.
MMSD2.0
Improved version of the multimodal sarcasm dataset.
Used to evaluate models' sarcasm detection capabilities.
Uncertainty Modeling
Modeling approach that considers sarcasm's subjectivity and diversity.
Used to enhance models' sarcasm understanding capabilities.
Open Questions Unanswered questions from this research
- 1 How to improve models' ability to recognize neutral samples?
- 2 How to reduce models' sensitivity to prompt variations?
Applications
Immediate Applications
Social Media Analysis
Helps understand sarcasm in user expressions, improving sentiment analysis.
Long-term Vision
Intelligent Assistant
Develop assistants capable of understanding complex human expressions, enhancing human-computer interaction.
Abstract
With the advent of large vision-language models (LVLMs) demonstrating increasingly human-like abilities, a pivotal question emerges: do different LVLMs interpret multimodal sarcasm differently, and can a single model grasp sarcasm from multiple perspectives like humans? To explore this, we introduce an analytical framework using systematically designed prompts on existing multimodal sarcasm datasets. Evaluating 12 state-of-the-art LVLMs over 2,409 samples, we examine interpretive variations within and across models, focusing on confidence levels, alignment with dataset labels, and recognition of ambiguous "neutral" cases. We further validate our findings on a diverse 100-sample mini-benchmark, incorporating multiple datasets, expanded prompt variants, and representative commercial LVLMs. Our findings reveal notable discrepancies -- across LVLMs and within the same model under varied prompts. While classification-oriented prompts yield higher internal consistency, models diverge markedly when tasked with interpretive reasoning. These results challenge binary labeling paradigms by highlighting sarcasm's subjectivity. We advocate moving beyond rigid annotation schemes toward multi-perspective, uncertainty-aware modeling, offering deeper insights into multimodal sarcasm comprehension. Our code and data are available at: https://github.com/CoderChen01/LVLMSarcasmAnalysis