---
title: "SMILE-Next — paper by Jung-Mok et al. (2026)"
description: "The authors present SMILE-Next, an extension of the earlier SMILE benchmark. 55 references."
authors: ["Lee Jung-Mok", "Kim Sung-Bin", "Joohyun Chang", "Lee Hyun", "Tae-Hyun Oh"]
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.2023/"
doi: "10.18653/v1/2026.acl-long.2023"
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/mok-2026-smile-next-teaching-large-language/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/mok-2026-smile-next-teaching-large-language/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/43/) / SMILE-Next: Teaching Large Language Models to Detect, Classify, and Reason about Laughter

# SMILE-Next: Teaching Large Language Models to Detect, Classify, and Reason about Laughter

**Authors:** Lee Jung-Mok, Kim Sung-Bin, Joohyun Chang, Lee Hyun and Tae-Hyun Oh · **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.2023/) · [PDF](https://aclanthology.org/2026.acl-long.2023.pdf) · [DOI 10.18653/v1/2026.acl-long.2023](https://doi.org/10.18653/v1/2026.acl-long.2023) · [OpenAlex](https://openalex.org/W7166830349)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Most earlier work on humor and laughter handles a single narrow task, such as spotting jokes in sitcoms or explaining cartoon humor. It rarely addresses laughter as it occurs in everyday human interaction. The authors present SMILE-Next, an extension of the earlier SMILE benchmark. It pairs videos from talk shows, sitcoms, interviews, movies and dyadic conversations with question–answer annotations for three tasks: detecting whether a clip induces laughter, classifying the laughter type, and explaining its cause in free text. Off-the-shelf tools first convert visual and acoustic cues into text, which an LLM then reasons over. Two additions strengthen the model. A laughter-focused Self-Instruct pipeline automatically produces extra training instructions. Mixture-of-Laugh-Experts (MoLE), a LoRA-based expert routing scheme, adapts to each task. Together these outperform multimodal LLM baselines, and textualized inputs work better than raw multimodal features. The work moves toward socially aware AI that can interpret why people laugh.

## Key points
- **Dataset size:** SMILE-Next has 2,384 detection samples, 1,957 type-classification samples and 2,045 reasoning samples, each split into train, validation and test sets.
- **Laughter types:** The categories are mirthful, polite and satirical, following the taxonomy of Tanaka and Campbell (2014). Satirical covers sarcastic, awkward and mocking laughter.
- **Router behavior:** Expert 1 dominates across all tasks. Classification shifts weight toward Expert 2 and detection slightly toward Expert 3.
- **Overhead and robustness:** MoLE adds about 19 ms per sample on average (roughly 1–2%) compared with a single expert. Reasoning stays stable with up to about 30% noise in facial action units and 40–50% noise in acoustic features.
- **Limitation:** The content is mainly English, so other languages and culture-specific laughter are underrepresented.

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

## Institutions

- [Korea Advanced Institute of Science and Technology](https://md.research.elamur.ai/free/en/universities/korea-advanced-institute-of-science-and-technology/) — University, Daejeon, South Korea · 51 works · 3,167 citations
- [Pohang University of Science and Technology](https://md.research.elamur.ai/free/en/universities/pohang-university-of-science-and-technology/) — University, Pohang, South Korea · 7 works · 464 citations

📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 2,869 works · [💭 Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/) · 1,552 works · [💭 Social Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/social-psychology/) · 306 works

🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,320 works

## Cite

Cite as: Lee Jung-Mok, Kim Sung-Bin, Joohyun Chang, Lee Hyun and Tae-Hyun Oh. 2026. SMILE-Next: Teaching Large Language Models to Detect, Classify, and Reason about Laughter. 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{jungmok2026smile,
  title = {{SMILE-Next: Teaching Large Language Models to Detect, Classify, and Reason about Laughter}},
  author = {Lee Jung-Mok, Kim Sung-Bin, Joohyun Chang, Lee Hyun and Tae-Hyun Oh},
  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.2023},
  url = {https://doi.org/10.18653/v1/2026.acl-long.2023}
}
```

- [← previous Smelling the Past: Investigating Historical Models for Olfactory Event Extraction](https://md.research.elamur.ai/free/en/papers/paccosi-2026-smelling-past-investigating-historical-models/)
- [next → SN-WER: Script-Normalized WER for Multi-Script Indic ASR Evaluation](https://md.research.elamur.ai/free/en/papers/pattnayak-2026-sn-wer-script-normalized-wer/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/43/)

## References

55 references, 22 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, and 1 others. 2023. [Gpt-4 technical report](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/). arXiv preprint arXiv:2303.08774.
2. Khalid Alnajjar, Mika Hämäläinen, Jörg Tiedemann, Jorma Laaksonen, and Mikko Kurimo. 2022. When to laugh and how hard? a multimodal approach to detecting humor and its intensity. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6875–6886, Gyeongju, Republic of… [find](https://scholar.google.com/scholar?q=Khalid+Alnajjar%2C+Mika+H%C3%A4m%C3%A4l%C3%A4inen%2C+J%C3%B6rg+Tiedemann%2C+Jorma+Laaksonen%2C+and+Mikko+Kurimo%2E+2022%2E+When+to+laugh+and+how+hard%3F+a+multimodal+approach+to+detecting+humor+and+its+intensity%2E+In+Proceedings+of+the)
3. Issa Annamoradnejad and Gohar Zoghi. 2020. Colbert: Using bert sentence embedding for humor detection. arXiv preprint arXiv:2004.12765. — [arXiv:2004.12765](https://arxiv.org/abs/2004.12765) [find](https://scholar.google.com/scholar?q=Issa+Annamoradnejad+and+Gohar+Zoghi%2E+2020%2E+Colbert%3A+Using+bert+sentence+embedding+for+humor+detection%2E+arXiv+preprint+arXiv%3A2004%2E12765%2E+%E2%80%94+%5BarXiv%3A2004%2E12765%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2004%2E12765%29)
4. Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, and 1 others. 2025. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502.13923. — [Qwen2.5-VL Technical Report](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen2-5-vl-technical/)
5. Max Bain, Jaesung Huh, Tengda Han, and Andrew Zisserman. 2023. Whisperx: Time-accurate speech transcription of long-form audio. arXiv preprint arXiv:2303.00747. [find](https://scholar.google.com/scholar?q=Max+Bain%2C+Jaesung+Huh%2C+Tengda+Han%2C+and+Andrew+Zisserman%2E+2023%2E+Whisperx%3A+Time%2Daccurate+speech+transcription+of+long%2Dform+audio%2E+arXiv+preprint+arXiv%3A2303%2E00747%2E)
6. William Sims Bainbridge, Edward E Brent, Kathleen M Carley, David R Heise, Michael W Macy, Barry Markovsky, and John Skvoretz. 1994. Artificial social intelligence. Annual review of sociology. [find](https://scholar.google.com/scholar?q=William+Sims+Bainbridge%2C+Edward+E+Brent%2C+Kathleen+M+Carley%2C+David+R+Heise%2C+Michael+W+Macy%2C+Barry+Markovsky%2C+and+John+Skvoretz%2E+1994%2E+Artificial+social+intelligence%2E+Annual+review+of+sociology%2E)
7. Satanjeev Banerjee and Alon Lavie. 2005. [METEOR: An automatic metric for MT evaluation with improved correlation with human judgments](https://md.research.elamur.ai/free/en/papers/banerjee-2005-meteor-automatic-metric-mt-evaluation/). In Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization, pages 65–72, Ann Arbor, Michigan. Ass…
8. Dario Bertero and Pascale Fung. 2016. Deep learning of audio and language features for humor prediction. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pages 496–501, Portorož, Slovenia. European Language Resources Association (ELRA). [find](https://scholar.google.com/scholar?q=Dario+Bertero+and+Pascale+Fung%2E+2016%2E+Deep+learning+of+audio+and+language+features+for+humor+prediction%2E+In+Proceedings+of+the+Tenth+International+Conference+on+Language+Resources+and+Evaluation+%28LREC)
9. Shyamal Buch, Cristóbal Eyzaguirre, Adrien Gaidon, Jiajun Wu, Li Fei-Fei, and Juan Carlos Niebles. 2022. Revisiting the" video" in video-language understanding. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). [find](https://scholar.google.com/scholar?q=Shyamal+Buch%2C+Crist%C3%B3bal+Eyzaguirre%2C+Adrien+Gaidon%2C+Jiajun+Wu%2C+Li+Fei%2DFei%2C+and+Juan+Carlos+Niebles%2E+2022%2E+Revisiting+the%22+video%22+in+video%2Dlanguage+understanding%2E+In+IEEE+Conference+on+Computer+Vision+a)
10. Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, Roger Zimmermann, Rada Mihalcea, and Soujanya Poria. 2019. [Towards multimodal sarcasm detection (an Obviously perfect paper)](https://md.research.elamur.ai/free/en/papers/castro-2019-towards-multimodal-sarcasm-detection-obviously/). In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4619–4629, Floren…
11. Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. 2023. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality. [find](https://scholar.google.com/scholar?q=Wei%2DLin+Chiang%2C+Zhuohan+Li%2C+Zi+Lin%2C+Ying+Sheng%2C+Zhanghao+Wu%2C+Hao+Zhang%2C+Lianmin+Zheng%2C+Siyuan+Zhuang%2C+Yonghao+Zhuang%2C+Joseph+E%2E+Gonzalez%2C+Ion+Stoica%2C+and+Eric+P%2E+Xing%2E+2023%2E+Vicuna%3A+An+open%2Dsource+cha)
12. Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, and 1 others. 2022. [Palm: Scaling language modeling with pathways](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/). arXiv preprint arXiv:2204.02311.
13. Kerstin Dautenhahn. 2007. Socially intelligent robots: dimensions of human–robot interaction. Philosophical transactions of the royal society B: Biological sciences. [find](https://scholar.google.com/scholar?q=Kerstin+Dautenhahn%2E+2007%2E+Socially+intelligent+robots%3A+dimensions+of+human%E2%80%93robot+interaction%2E+Philosophical+transactions+of+the+royal+society+B%3A+Biological+sciences%2E)
14. Najim Dehak, Pierre Dumouchel, and Patrick Kenny. 2007. Modeling prosodic features with joint factor analysis for speaker verification. IEEE Transactions on Audio, Speech, and Language Processing. [find](https://scholar.google.com/scholar?q=Najim+Dehak%2C+Pierre+Dumouchel%2C+and+Patrick+Kenny%2E+2007%2E+Modeling+prosodic+features+with+joint+factor+analysis+for+speaker+verification%2E+IEEE+Transactions+on+Audio%2C+Speech%2C+and+Language+Processing%2E)
15. Shihan Dou, Enyu Zhou, Yan Liu, Songyang Gao, Jun Zhao, Wei Shen, Yuhao Zhou, Zhiheng Xi, Xiao Wang, Xiaoran Fan, and 1 others. 2023. Loramoe: Alleviate world knowledge forgetting in large language models via moe-style plugin. arXiv preprint arXiv:2312.09979. — [arXiv:2312.09979](https://arxiv.org/abs/2312.09979) [find](https://scholar.google.com/scholar?q=Shihan+Dou%2C+Enyu+Zhou%2C+Yan+Liu%2C+Songyang+Gao%2C+Jun+Zhao%2C+Wei+Shen%2C+Yuhao+Zhou%2C+Zhiheng+Xi%2C+Xiao+Wang%2C+Xiaoran+Fan%2C+and+1+others%2E+2023%2E+Loramoe%3A+Alleviate+world+knowledge+forgetting+in+large+language+mo)
16. Nan Du, Yanping Huang, Andrew M Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, and 1 others. 2022. [Glam: Efficient scaling of language models with mixture-of-experts](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/). In International Conference on Machine Learning (ICML), pages 5547– 5569.
17. William Fedus, Barret Zoph, and Noam Shazeer. 2022. Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity. Journal of Machine Learning Research, 23(120):1–39. [find](https://scholar.google.com/scholar?q=William+Fedus%2C+Barret+Zoph%2C+and+Noam+Shazeer%2E+2022%2E+Switch+transformers%3A+Scaling+to+trillion+parameter+models+with+simple+and+efficient+sparsity%2E+Journal+of+Machine+Learning+Research%2C+23%28120%29%3A1%E2%80%9339%2E)
18. Jon Gillick, Wesley Deng, Kimiko Ryokai, and David Bamman. 2021. Robust laughter detection in noisy environments. In Conference of the International Speech Communication Association (Interspeech). [find](https://scholar.google.com/scholar?q=Jon+Gillick%2C+Wesley+Deng%2C+Kimiko+Ryokai%2C+and+David+Bamman%2E+2021%2E+Robust+laughter+detection+in+noisy+environments%2E+In+Conference+of+the+International+Speech+Communication+Association+%28Interspeech%29%2E)
19. Yunhao Gou, Zhili Liu, Kai Chen, Lanqing Hong, Hang Xu, Aoxue Li, Dit-Yan Yeung, James T Kwok, and Yu Zhang. 2023. Mixture of cluster-conditional lora experts for vision-language instruction tuning. arXiv preprint arXiv:2312.12379. — [arXiv:2312.12379](https://arxiv.org/abs/2312.12379) [find](https://scholar.google.com/scholar?q=Yunhao+Gou%2C+Zhili+Liu%2C+Kai+Chen%2C+Lanqing+Hong%2C+Hang+Xu%2C+Aoxue+Li%2C+Dit%2DYan+Yeung%2C+James+T+Kwok%2C+and+Yu+Zhang%2E+2023%2E+Mixture+of+cluster%2Dconditional+lora+experts+for+vision%2Dlanguage+instruction+tuning%2E+a)
20. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, and 1 others. 2024. [The llama 3 herd of models](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/). arXiv e-prints, pages arXiv–2407.
21. Md Kamrul Hasan, Md Saiful Islam, Sangwu Lee, Wasifur Rahman, Iftekhar Naim, Mohammed Ibrahim Khan, and Ehsan Hoque. 2023. Textmi: Textualize multimodal information for integrating non-verbal cues in pre-trained language models. arXiv preprint arXiv:2303.15430. — [arXiv:2303.15430](https://arxiv.org/abs/2303.15430) [find](https://scholar.google.com/scholar?q=Md+Kamrul+Hasan%2C+Md+Saiful+Islam%2C+Sangwu+Lee%2C+Wasifur+Rahman%2C+Iftekhar+Naim%2C+Mohammed+Ibrahim+Khan%2C+and+Ehsan+Hoque%2E+2023%2E+Textmi%3A+Textualize+multimodal+information+for+integrating+non%2Dverbal+cues+in+)
22. Md Kamrul Hasan, Wasifur Rahman, AmirAli Bagher Zadeh, Jianyuan Zhong, Md Iftekhar Tanveer, Louis-Philippe Morency, and Mohammed (Ehsan) Hoque. 2019. UR-FUNNY: A multimodal language dataset for understanding humor. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Proces… [find](https://scholar.google.com/scholar?q=Md+Kamrul+Hasan%2C+Wasifur+Rahman%2C+AmirAli+Bagher+Zadeh%2C+Jianyuan+Zhong%2C+Md+Iftekhar+Tanveer%2C+Louis%2DPhilippe+Morency%2C+and+Mohammed+%28Ehsan%29+Hoque%2E+2019%2E+UR%2DFUNNY%3A+A+multimodal+language+dataset+for+unders)
23. Jack Hessel, Ana Marasovic, Jena D. Hwang, Lillian Lee, Jeff Da, Rowan Zellers, Robert Mankoff, and Yejin Choi. 2023. [Do androids laugh at electric sheep?](https://md.research.elamur.ai/free/en/papers/hessel-2023-do-androids-laugh-electric-sheep/) humor ‘understanding” benchmarks from the new yorker caption contest. In Proceedings of the 61st Annual Meeting of the Association for Computatio…
24. Lee Hyun, Kim Sung-Bin, Seungju Han, Youngjae Yu, and Tae-Hyun Oh. 2024. Smile: Multimodal dataset for understanding laughter in video with language models. In NAACL-HLT (Findings), pages 1149– 1167. [find](https://scholar.google.com/scholar?q=Lee+Hyun%2C+Kim+Sung%2DBin%2C+Seungju+Han%2C+Youngjae+Yu%2C+and+Tae%2DHyun+Oh%2E+2024%2E+Smile%3A+Multimodal+dataset+for+understanding+laughter+in+video+with+language+models%2E+In+NAACL%2DHLT+%28Findings%29%2C+pages+1149%E2%80%93+1167%2E)
25. Dayoon Ko, Sangho Lee, and Gunhee Kim. 2023. Can language models laugh at youtube short-form videos? In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2897–2916. [find](https://scholar.google.com/scholar?q=Dayoon+Ko%2C+Sangho+Lee%2C+and+Gunhee+Kim%2E+2023%2E+Can+language+models+laugh+at+youtube+short%2Dform+videos%3F+In+Proceedings+of+the+2023+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pages+28)
26. Jie Lei, Licheng Yu, Mohit Bansal, and Tamara Berg. 2018. [TVQA: Localized, compositional video question answering](https://md.research.elamur.ai/free/en/papers/lei-2018-tvqa-localized-compositional-video-question/). In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1369–1379, Brussels, Belgium. Association for Computational Linguistics.
27. Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, and Zhifeng Chen. 2021. Gshard: Scaling giant models with conditional computation and automatic sharding. In International Conference on Learning Representations (ICLR). [find](https://scholar.google.com/scholar?q=Dmitry+Lepikhin%2C+HyoukJoong+Lee%2C+Yuanzhong+Xu%2C+Dehao+Chen%2C+Orhan+Firat%2C+Yanping+Huang%2C+Maxim+Krikun%2C+Noam+Shazeer%2C+and+Zhifeng+Chen%2E+2021%2E+Gshard%3A+Scaling+giant+models+with+conditional+computation+and)
28. Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang. 2019. [Visualbert: A simple and performant baseline for vision and language](https://md.research.elamur.ai/free/en/papers/li-2019-visualbert-simple-performant-baseline-vision/). arXiv preprint arXiv:1908.03557.
29. Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, and Li Yuan. 2023. [Video-llava: Learning united visual representation by alignment before projection](https://md.research.elamur.ai/free/en/papers/lin-2023-video-llava-learning-united-visual/). arXiv preprint arXiv:2311.10122.
30. Chin-Yew Lin. 2004. [ROUGE: A package for automatic evaluation of summaries](https://md.research.elamur.ai/free/en/papers/lin-2004-rouge-package-automatic-evaluation-summaries/). In Text Summarization Branches Out, pages 74–81, Barcelona, Spain. Association for Computational Linguistics.
31. Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. 2019. [Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks](https://md.research.elamur.ai/free/en/papers/lu-2019-vilbert-pretraining-task-agnostic-visiolinguistic/). In Advances in Neural Information Processing Systems (NeurIPS).
32. Anton Nijholt, Andreea I Niculescu, Alessandro Valitutti, and Rafael E Banchs. 2017. Humor in human-computer interaction: a short survey. Adjunct Proceedings of INTERACT. [find](https://scholar.google.com/scholar?q=Anton+Nijholt%2C+Andreea+I+Niculescu%2C+Alessandro+Valitutti%2C+and+Rafael+E+Banchs%2E+2017%2E+Humor+in+human%2Dcomputer+interaction%3A+a+short+survey%2E+Adjunct+Proceedings+of+INTERACT%2E)
33. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. [Bleu: a method for automatic evaluation of machine translation](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/). In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pages 311–318, Philadelphia, Pennsylvania, USA. Association for Computational…
34. Anupama Ray, Shubham Mishra, Apoorva Nunna, and Pushpak Bhattacharyya. 2022. [A multimodal corpus for emotion recognition in sarcasm](https://md.research.elamur.ai/free/en/papers/ray-2022-multimodal-corpus-emotion-recognition-sarcasm/). In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6992–7003, Marseille, France. European Language Resources Association.
35. Nils Reimers and Iryna Gurevych. 2019. [Sentence-BERT: Sentence embeddings using Siamese BERT-networks](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/). In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3982–3992.
36. Nicolas Richet, Soufiane Belharbi, Haseeb Aslam, Meike Emilie Schadt, Manuela González-González, Gustave Cortal, Alessandro Lameiras Koerich, Marco Pedersoli, Alain Finkel, Simon Bacon, and 1 others. 2024. Textualized and feature-based models for compound multimodal emotion recognition in the wild.… — [arXiv:2407.12927](https://arxiv.org/abs/2407.12927) [find](https://scholar.google.com/scholar?q=Nicolas+Richet%2C+Soufiane+Belharbi%2C+Haseeb+Aslam%2C+Meike+Emilie+Schadt%2C+Manuela+Gonz%C3%A1lez%2DGonz%C3%A1lez%2C+Gustave+Cortal%2C+Alessandro+Lameiras+Koerich%2C+Marco+Pedersoli%2C+Alain+Finkel%2C+Simon+Bacon%2C+and+1+others%2E+)
37. Sophie K Scott, Nadine Lavan, Sinead Chen, and Carolyn McGettigan. 2014. The social life of laughter. Trends in cognitive sciences. [find](https://scholar.google.com/scholar?q=Sophie+K+Scott%2C+Nadine+Lavan%2C+Sinead+Chen%2C+and+Carolyn+McGettigan%2E+2014%2E+The+social+life+of+laughter%2E+Trends+in+cognitive+sciences%2E)
38. Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. 2017. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. In International Conference on Learning Representations (ICLR). [find](https://scholar.google.com/scholar?q=Noam+Shazeer%2C+Azalia+Mirhoseini%2C+Krzysztof+Maziarz%2C+Andy+Davis%2C+Quoc+Le%2C+Geoffrey+Hinton%2C+and+Jeff+Dean%2E+2017%2E+Outrageously+large+neural+networks%3A+The+sparsely%2Dgated+mixture%2Dof%2Dexperts+layer%2E+In+Inter)
39. Sheng Shen, Le Hou, Yanqi Zhou, Nan Du, Shayne Longpre, Jason Wei, Hyung Won Chung, Barret Zoph, William Fedus, Xinyun Chen, Tu Vu, Yuexin Wu, Wuyang Chen, Albert Webson, Yunxuan Li, Vincent Y Zhao, Hongkun Yu, Kurt Keutzer, Trevor Darrell, and Denny Zhou. 2024. [Mixture-of-experts meets instruction…](https://md.research.elamur.ai/free/en/papers/shen-2023-mixture-experts-meets-instruction-tuning/)
40. David Stauffer. 1999. Let the good times roll: Building a fun culture. Harvard Management Update. [find](https://scholar.google.com/scholar?q=David+Stauffer%2E+1999%2E+Let+the+good+times+roll%3A+Building+a+fun+culture%2E+Harvard+Management+Update%2E)
41. Hiroki Tanaka and Nick Campbell. 2014. Classification of social laughter in natural conversational speech. Computer Speech & Language, 28(1):314–325. [find](https://scholar.google.com/scholar?q=Hiroki+Tanaka+and+Nick+Campbell%2E+2014%2E+Classification+of+social+laughter+in+natural+conversational+speech%2E+Computer+Speech+%26+Language%2C+28%281%29%3A314%E2%80%93325%2E)
42. Ruijie Tao, Zexu Pan, Rohan Kumar Das, Xinyuan Qian, Mike Zheng Shou, and Haizhou Li. 2021. Is someone speaking? exploring long-term temporal features for audio-visual active speaker detection. In ACM International Conference on Multimedia (MM). [find](https://scholar.google.com/scholar?q=Ruijie+Tao%2C+Zexu+Pan%2C+Rohan+Kumar+Das%2C+Xinyuan+Qian%2C+Mike+Zheng+Shou%2C+and+Haizhou+Li%2E+2021%2E+Is+someone+speaking%3F+exploring+long%2Dterm+temporal+features+for+audio%2Dvisual+active+speaker+detection%2E+In+ACM)
43. Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, and 1 others. 2024. [Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution](https://md.research.elamur.ai/free/en/papers/wang-2024-qwen2-vl-enhancing-vision/). arXiv preprint arXiv:2409.12191.
44. Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A Smith, Daniel Khashabi, and Hannaneh Hajishirzi. 2022a. Self-instruct: Aligning language models with self-generated instructions. arXiv preprint arXiv:2212.10560. [find](https://scholar.google.com/scholar?q=Yizhong+Wang%2C+Yeganeh+Kordi%2C+Swaroop+Mishra%2C+Alisa+Liu%2C+Noah+A+Smith%2C+Daniel+Khashabi%2C+and+Hannaneh+Hajishirzi%2E+2022a%2E+Self%2Dinstruct%3A+Aligning+language+models+with+self%2Dgenerated+instructions%2E+arXiv+p)
45. Binzhu Xie, Sicheng Zhang, Zitang Zhou, Bo Li, Yuanhan Zhang, Jack Hessel, Jingkang Yang, and Ziwei Liu. 2024. Funqa: Towards surprising video comprehension. In European Conference on Computer Vision, pages 39–57. Springer. [find](https://scholar.google.com/scholar?q=Binzhu+Xie%2C+Sicheng+Zhang%2C+Zitang+Zhou%2C+Bo+Li%2C+Yuanhan+Zhang%2C+Jack+Hessel%2C+Jingkang+Yang%2C+and+Ziwei+Liu%2E+2024%2E+Funqa%3A+Towards+surprising+video+comprehension%2E+In+European+Conference+on+Computer+Vision%2C)
46. Jin Xu, Zhifang Guo, Jinzheng He, Hangrui Hu, Ting He, Shuai Bai, Keqin Chen, Jialin Wang, Yang Fan, Kai Dang, and 1 others. 2025. Qwen2. 5-omni technical report. arXiv preprint arXiv:2503.20215. — [Qwen2.5-Omni Technical Report](https://md.research.elamur.ai/free/en/papers/xu-2025-qwen2-5-omni-technical/)
47. Li Yao, Yan Wan, Hongjie Ni, and Bugao Xu. 2021. Action unit classification for facial expression recognition using active learning and svm. Multimedia Tools and Applications. [find](https://scholar.google.com/scholar?q=Li+Yao%2C+Yan+Wan%2C+Hongjie+Ni%2C+and+Bugao+Xu%2E+2021%2E+Action+unit+classification+for+facial+expression+recognition+using+active+learning+and+svm%2E+Multimedia+Tools+and+Applications%2E)
48. Yuan Yao, Tianyu Yu, Ao Zhang, Chongyi Wang, Junbo Cui, Hongji Zhu, Tianchi Cai, Haoyu Li, Weilin Zhao, Zhihui He, and 1 others. 2024. [Minicpm-v: A gpt-4v level mllm on your phone](https://md.research.elamur.ai/free/en/papers/yao-2024-minicpm-v-gpt-4v/). arXiv preprint arXiv:2408.01800.
49. Ted Zadouri, Ahmet Üstün, Arash Ahmadian, Beyza Ermis, Acyr Locatelli, and Sara Hooker. 2024. [Pushing mixture of experts to the limit: Extremely parameter efficient moe for instruction tuning](https://md.research.elamur.ai/free/en/papers/zadouri-2023-pushing-mixture-experts-limit-extremely/). In International Conference on Learning Representations (ICLR).
50. Andy Zeng, Adrian Wong, Stefan Welker, Krzysztof Choromanski, Federico Tombari, Aveek Purohit, Michael Ryoo, Vikas Sindhwani, Johnny Lee, Vincent Vanhoucke, and 1 others. 2022. [Socratic models: Composing zero-shot multimodal reasoning with language](https://md.research.elamur.ai/free/en/papers/zeng-2022-socratic-models-composing-zero-shot/). In International Conference on Learning Representa…
51. Zhenhailong Wang, Manling Li, Ruochen Xu, Luowei Zhou, Jie Lei, Xudong Lin, Shuohang Wang, Ziyi Yang, Chenguang Zhu, Derek Hoiem, and 1 others. 2022b. Language models with image descriptors are strong few-shot video-language learners. In Advances in Neural Information Processing Systems (NeurIPS). [find](https://scholar.google.com/scholar?q=Zhenhailong+Wang%2C+Manling+Li%2C+Ruochen+Xu%2C+Luowei+Zhou%2C+Jie+Lei%2C+Xudong+Lin%2C+Shuohang+Wang%2C+Ziyi+Yang%2C+Chenguang+Zhu%2C+Derek+Hoiem%2C+and+1+others%2E+2022b%2E+Language+models+with+image+descriptors+are+strong)
52. Melissa B Wanzer, Ann B Frymier, and Jeffrey Irwin. 2010. An explanation of the relationship between instructor humor and student learning: Instructional humor processing theory. Communication education. [find](https://scholar.google.com/scholar?q=Melissa+B+Wanzer%2C+Ann+B+Frymier%2C+and+Jeffrey+Irwin%2E+2010%2E+An+explanation+of+the+relationship+between+instructor+humor+and+student+learning%3A+Instructional+humor+processing+theory%2E+Communication+educati)
53. Caleb Warren, Adam Barsky, and A Peter McGraw. 2021. What makes things funny? an integrative review of the antecedents of laughter and amusement. Personality and Social Psychology Review. [find](https://scholar.google.com/scholar?q=Caleb+Warren%2C+Adam+Barsky%2C+and+A+Peter+McGraw%2E+2021%2E+What+makes+things+funny%3F+an+integrative+review+of+the+antecedents+of+laughter+and+amusement%2E+Personality+and+Social+Psychology+Review%2E)
54. Orion Weller and Kevin Seppi. 2020. The rJokes dataset: a large scale humor collection. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 6136–6141, Marseille, France. European Language Resources Association. [find](https://scholar.google.com/scholar?q=Orion+Weller+and+Kevin+Seppi%2E+2020%2E+The+rJokes+dataset%3A+a+large+scale+humor+collection%2E+In+Proceedings+of+the+Twelfth+Language+Resources+and+Evaluation+Conference%2C+pages+6136%E2%80%936141%2C+Marseille%2C+France%2E+)
55. Jessica Williams, Stephen M Fiore, and Florian Jentsch. 2022. Supporting artificial social intelligence with theory of mind. Frontiers in artificial intelligence. [find](https://scholar.google.com/scholar?q=Jessica+Williams%2C+Stephen+M+Fiore%2C+and+Florian+Jentsch%2E+2022%2E+Supporting+artificial+social+intelligence+with+theory+of+mind%2E+Frontiers+in+artificial+intelligence%2E)

*Updated 1 November 2026*