Papers by Zhe Tan
We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning? (2025.acl-long)
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Runqi Qiao, Qiuna Tan, Guanting Dong, MinhuiWu MinhuiWu, Chong Sun, Xiaoshuai Song, Jiapeng Wang, Zhuoma GongQue, Shanglin Lei, YiFan Zhang, Zhe Wei, Miaoxuan Zhang, Runfeng Qiao, Xiao Zong, Yida Xu, Peiqing Yang, Zhimin Bao, Muxi Diao, Chen Li, Honggang Zhang
| Challenge: | Existing benchmarks focus more on end-to-end performance, but neglect the underlying principles of knowledge acquisition and generalization. |
| Approach: | They propose a benchmark specifically designed to explore the problem-solving principles by decomposing 6.5K visual math problems into 10.9K step-level questions for evaluation. |
| Outcome: | The proposed benchmark covers 6.5K visual math problems and 10.9K step-level questions spanning 5 layers of knowledge granularity and 67 hierarchical knowledge concepts. |
CuMA: Aligning LLMs with Sparse Cultural Values via Demographic-Aware Mixture of Adapters (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have a global audience, so alignment must extend to cultural resonance. |
| Approach: | They propose a framework that frames alignment as a conditional capacity separation problem. |
| Outcome: | The proposed framework outperforms both dense baselines and semantic-only MoEs on three large language models. |
Expressing Visual Relationships via Language (P19-1)
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| Challenge: | Current studies on image captioning focus on single image, but there are no effective models for generating relational captions for two images. |
| Approach: | They propose a language-guided image editing dataset that contains real image pairs with corresponding editing instructions. |
| Outcome: | The proposed model outperforms baseline and existing methods on two datasets. |
LAMCL: A Length-aware Momentum Contrastive Learning Framework for Multiscale Machine-Revised Text Detection (2026.acl-long)
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| Challenge: | Recent detection methods struggle to capture fine-grained semantic differences, especially for short texts. |
| Approach: | They propose a framework for machine-revised text detection that integrates two modules to enhance discriminative semantic features. |
| Outcome: | The proposed method outperforms existing detectors in identifying machine-revised text across diverse practical scenarios, tasks, and LLMs. |
REACT: Representation Extraction And Controllable Tuning to Overcome Overfitting in LLM Knowledge Editing (2025.emnlp-main)
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Haitian Zhong, Yuhuan Liu, Ziyang Xu, Guofan Liu, Qiang Liu, Shu Wu, Zhe Zhao, Liang Wang, Tieniu Tan
| Challenge: | Large language model editing methods suffer from overfitting, where factual updates can propagate beyond their intended scope, overemphasizing the edited target even when it’s contextually inappropriate. |
| Approach: | They propose a framework for precise and controllable knowledge editing that utilizes two-phase representations and a linear transformation to compute a directional "belief shift" vector. |
| Outcome: | The proposed framework significantly reduces overfitting across nearly all evaluation metrics and on COUNTERFACT and MQuAKE. |