# MultiPun: Can Large Vision-Language Models Understand Multimodal Puns?

> This article introduces MultiPun, a paper accepted at the ACL 2026 main conference, which explores the ability of large vision-language models (LVLMs) to understand image-text combined puns, revealing the limitations and challenges of current models in capturing cross-modal humor and ambiguity.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-09T07:11:21.000Z
- 最近活动: 2026-04-09T07:15:25.759Z
- 热度: 159.9
- 关键词: 视觉语言模型, 多模态理解, 双关语, 幽默理解, ACL 2026, 语义推理, 跨模态对齐, 大模型评测
- 页面链接: https://www.zingnex.cn/en/forum/thread/multipun
- Canonical: https://www.zingnex.cn/forum/thread/multipun
- Markdown 来源: floors_fallback

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## [Main Floor] MultiPun: Can Large Vision-Language Models Understand Multimodal Puns?

This article introduces MultiPun, a paper accepted at the ACL 2026 main conference, which explores the ability of large vision-language models (LVLMs) to understand image-text combined puns, revealing the limitations and challenges of current models in capturing cross-modal humor and ambiguity.

## Research Background: The Intersection of Humor and Multimodal AI

Puns are exquisite rhetorical devices in human language that use polysemy or similar pronunciation to create humor. When puns are combined with images, it requires simultaneous processing of visual and textual information and establishing connections, which increases the difficulty of understanding. The MultiPun project addresses this challenge by systematically evaluating the ability of mainstream LVLMs to understand multimodal puns.

## Definition and Typical Cases of Multimodal Puns

Multimodal puns rely on image-text interaction to generate humor, with main types including:
- Visual-text puns: The image provides the literal meaning, while the text offers another layer of meaning
- Context-dependent: Requires specific cultural background knowledge
- Ambiguity resolution: The same content may have different interpretations

For example, a picture of a fish playing the piano paired with the text 'piano tuner'—the joke lies in the similar pronunciation of 'tuna' (a type of fish) and 'tuner' (someone who tunes pianos). This cross-modal semantic leap is a huge challenge for AI.

## MultiPun Dataset and Evaluation Framework

### Dataset Construction
The dataset built by the research team has the following characteristics:
1. Diversity: Covers different pun types such as homophones and polysemy
2. Difficulty levels: From simple cases to complex ones requiring deep cultural knowledge
3. Manual verification: Ensures samples have clear humorous intent and reasonable explanations

### Evaluation Dimensions
Multi-dimensional evaluation is designed:
- Understanding ability: Can it identify the existence of a pun?
- Explanation ability: Can it explain the joke?
- Generation ability: Can it create pun text given an image?

## Experimental Findings: Performance and Limitations of Mainstream Models

### Key Findings
Tests on GPT-4V, Gemini, Claude, etc., revealed:
1. Limited recognition rate: The best model's recognition accuracy is far lower than that of humans
2. Weak explanation ability: Guessing the answer but misunderstanding the humor mechanism
3. Obvious cultural dependence: Worse performance on puns involving specific cultures

### Failure Modes
Typical failures:
- Over-literalization: Missing metaphorical hints
- Modal separation: Difficulty in establishing deep image-text connections
- Lack of common sense: Missing world knowledge and cultural background

## Technical Depth: Core Challenges in Pun Understanding

### Semantic Leap Challenge
Understanding puns requires complex semantic reasoning: identifying multiple meanings, switching interpretations, and evaluating contextual rationality—this is a weak point of current large models.

### Complexity of Cross-Modal Alignment
Multimodal puns require non-trivial image-text connections: the image object is literally related to the text, while another meaning of the text contrasts with or supplements the image. Humor comes from the 'unexpected yet reasonable' connection. Such connections are scarce in training data, making it difficult for models to grasp the patterns.

## Research Significance and Future Application Directions

### Implications for AI Research
1. Evaluation benchmark: Provides a new dimension to assess the deep understanding ability of models
2. Ability boundary: Reveals the limitations of models in fine-grained semantic reasoning tasks
3. Improvement direction: Points out the importance of enhancing cross-modal reasoning and common sense understanding

### Potential Applications
- Creative assistance: Helping advertising and marketing generate and evaluate puns
- Content moderation: Identifying content that may cause misunderstandings due to cultural differences
- Educational applications: Developing tools for language learners to understand humor and idioms

## Conclusion: How Far is AI from Truly 'Getting the Joke'?

MultiPun uses humor as an entry point to reveal the challenges of LVLMs in deep semantic understanding. The paper's title metaphorically means 'I get your meaning', but current models are still quite far from truly understanding puns. This research points the way for future improvements.