---
title: "“I See What You Did There” — paper by Xu et al. (2026)"
description: "The authors build an automatic pipeline that mines homophonic word pairs and polysemous nouns from… 53 references, cited by 1 paper here."
authors: ["Naen Xu", "Jiayi Sheng", "Changjiang Li", "Chunyi Zhou", "Yuyuan Li", "Tianyu Du", "Jun Wang", "Zhihui Fu", "Jinbao Li", "Shouling Ji"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.444/"
doi: "10.18653/v1/2026.acl-long.444"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/xu-2026-i-see-what-you-did/"
---
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# “I See What You Did There”: Can Large Vision-Language Models Understand Multimodal Puns?

**Authors:** [Naen Xu](https://md.research.elamur.ai/free/en/authors/naen-xu/), Jiayi Sheng, [Changjiang Li](https://md.research.elamur.ai/free/en/authors/changjiang-li/), [Chunyi Zhou](https://md.research.elamur.ai/free/en/authors/chunyi-zhou/), Yuyuan Li, [Tianyu Du](https://md.research.elamur.ai/free/en/authors/tianyu-du/), [Jun Wang](https://md.research.elamur.ai/free/en/authors/jun-wang/), [Zhihui Fu](https://md.research.elamur.ai/free/en/authors/zhihui-fu/), [Jinbao Li](https://md.research.elamur.ai/free/en/authors/jinbao-li/) and [Shouling Ji](https://md.research.elamur.ai/free/en/authors/shouling-ji/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.444/) · [PDF](https://aclanthology.org/2026.acl-long.444.pdf) · [DOI 10.18653/v1/2026.acl-long.444](https://doi.org/10.18653/v1/2026.acl-long.444) · [OpenAlex](https://openalex.org/W7166873540)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper asks whether vision-language models (VLMs) actually understand multimodal puns or only react to playful-looking content. In these puns, an image shows a literal object acting out a second meaning, and the caption triggers that second meaning. Earlier benchmarks were text-only or contained only positive examples. They also used one-sided yes/no questions, which cannot separate real comprehension from a model's tendency to agree.

The authors build an automatic pipeline that mines homophonic word pairs and polysemous nouns from the CMU Pronouncing Dictionary and WordNet. GPT-4o then generates captions and image descriptions. Hard non-pun distractors are created by swapping the pun word. The resulting benchmark, MultiPun, is used to test 11 VLMs on detection, localization and explanation, with each question asked in both direct and reversed form.

Most models over-interpret: they label distractors as puns and explain the wordplay imprecisely. Two remedies are proposed:
- Pun-CoT, a structured verification prompt.
- Pun-Tuning, fine-tuning on balanced data.

Together they raise F1 by 16.5% on average. This is a useful diagnostic for cross-modal humor reasoning.

## Key points
- MultiPun contains 445 puns (homophonic and homographic) and 890 non-pun distractors, produced by explicative and random word substitution.
- Candidate pun tuples are filtered by:
  - Zipf frequency (>3.0 for homophones, >3.8 for homographs);
  - visually depictable WordNet noun categories;
  - lexical-file divergence and low path similarity (\<0.1).
- Prompts are given in both biased-to-pun and biased-to-non-pun versions to expose sycophancy and affirmative bias. TPR, TNR and F1 are reported.
- Pun-CoT doubles the homophonic TNR of Qwen3-VL-8B-Thinking, from 0.387 to 0.776.
- Pun-Tuning results on homophonic puns:
  - Qwen3-VL-30B-Instruct TNR rises from 0.209 to 0.948.
  - LLaVA-v1.6 Vicuna-13B F1 rises from 0.057 to 0.640.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [Hangzhou Dianzi University](https://md.research.elamur.ai/free/en/universities/hangzhou-dianzi-university/) — University, Hangzhou, China · 7 works · 1,061 citations
- [Qilu University of Technology](https://md.research.elamur.ai/free/en/universities/qilu-university-of-technology/) — University, Jinan, China · 4 works · 3 citations
- [Palo Alto Networks](https://md.research.elamur.ai/free/en/companies/palo-alto-networks/) — Company, Santa Clara, United States · 3 works · 2 citations

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## Cite

Cite as: Naen Xu, Jiayi Sheng, Changjiang Li, Chunyi Zhou, Yuyuan Li, Tianyu Du, Jun Wang, Zhihui Fu, Jinbao Li and Shouling Ji. 2026. “I See What You Did There”: Can Large Vision-Language Models Understand Multimodal Puns?. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{xu2026i,
  title = {{“I See What You Did There”: Can Large Vision-Language Models Understand Multimodal Puns?}},
  author = {Naen Xu, Jiayi Sheng, Changjiang Li, Chunyi Zhou, Yuyuan Li, Tianyu Du, Jun Wang, Zhihui Fu, Jinbao Li and Shouling Ji},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.444},
  url = {https://doi.org/10.18653/v1/2026.acl-long.444}
}
```

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*Updated 1 November 2026*