Papers by Junze Li
Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition (2021.acl-short)
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| Challenge: | Existing work on humor recognition does not examine the actual joke mechanism . a recent study focused on humor-specific stylistic features, but few have tried to establish a connection between them and humor theories. |
| Approach: | They propose to model the set-up and punchline as part developing semantic uncertainty and disrupt audience expectations. |
| Outcome: | The proposed features can tell jokes from non-jokes, compared with baselines. |
On-Policy Self-Distillation for Efficient Diffusion Language Models with Early-Stage Calibration (2026.findings-acl)
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Huaisheng Zhu, MingYu Liu, Junze Liu, Zhen Ge, Tian Wang, Jiri Gesi, Dakuo Wang, Weiqi Zhang, Houyu Zhang, Yufan Guo, Xian Li, Bing Yin, Sujay Sanghavi
| Challenge: | Recent studies have demonstrated that masked diffusion models (MDMs) can surpass autoregressive models (ARMs) in various tasks. |
| Approach: | They propose a method to calibrate early token predictions without demonstration data by distilling an unnormalized target distribution into the original model. |
| Outcome: | Experiments on math, planning, and RLHF tasks show that COPSD improves both effectiveness and efficiency, and further enhances performance when combined with supervised fine-tuning. |