Papers by Tuo Zhou
Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)
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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu
| Challenge: | Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature. |
| Approach: | They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management. |
| Outcome: | The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench. |
GENUINE: Graph Enhanced Multi-level Uncertainty Estimation for Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing methods for estimation of uncertainty overlook semantic dependencies, authors say . genUINE: Graph ENhanced mUlti-level uncertainty Estimation for Large Language Models leverages dependency parse trees and hierarchical graph pooling . |
| Approach: | They propose a graph-enhanced mUlti-level uncertaINty estimation framework that leverages dependency parse trees and hierarchical graph pooling to refine uncertainty quantification. |
| Outcome: | The proposed framework achieves higher AUROC and lower calibration errors than existing methods. |
RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining (2022.acl-long)
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| Challenge: | Large-scale pretrained language models have achieved SOTA results on NLP tasks but are vulnerable to adversarial attacks especially for logographic languages like Chinese. |
| Approach: | They propose a pretrained Chinese Bert that is robust to various forms of adversarial attacks like word perturbation, synonyms, typos, etc. |
| Outcome: | The proposed model outperforms baselines on 5 Chinese NLU tasks without sacrificing performance on clean testsets. |
MessToClean: Evidence-Grounded Structure-Preserving Reconstruction for Real-World Degraded Exam Paper Images (2026.acl-long)
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Jiayi Tuo, Cheng Tang, Zihan Wang, Chenyue Zhou, Yao Li, Yanbiao Ma, Chao Wang, Wei Dai, Mingxuan Wang, Shitong Qin, Ziwei Zhao
| Challenge: | Existing Multimodal Large Language Models (MLLMs) fail under RDEI, leading to disrupted structure and evidence-unsupported hallucinations. |
| Approach: | They propose a backbone-agnostic, evidence-driven pipeline that treats off-the-shelf MLLMs as interchangeable components to improve stem consistency and figure consistency. |
| Outcome: | The proposed pipeline improves stem consistency by 1.01-3.18%, figure consistency by 0.50-49.16%, and refusal F1 by 1.06-10.88% across question types. |