Papers by Dongsheng Ma
CodeFlowBench: A Multi-turn, Iterative Benchmark for Complex Code Generation (2026.acl-long)
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| Challenge: | Modern software development demands code that is maintainable, testable, and scalable by organizing the implementation into modular components with iterative reuse of existing codes. |
| Approach: | They propose a benchmark to evaluate LLMs' ability to perform codeflow by reusing existing functions over multiple turns. |
| Outcome: | The proposed benchmarks show that LLMs perform significantly worse in multi-turn codeflow scenarios and that their performance inversely correlates with dependency complexity. |
DocLLM: A Layout-Aware Generative Language Model for Multimodal Document Understanding (2024.acl-long)
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Dongsheng Wang, Natraj Raman, Mathieu Sibue, Zhiqiang Ma, Petr Babkin, Simerjot Kaur, Yulong Pei, Armineh Nourbakhsh, Xiaomo Liu
| Challenge: | Documents with rich layouts are a significant portion of enterprise corpora and document AI is still a challenge. |
| Approach: | They propose a lightweight extension to traditional large language models for reasoning over visual documents that takes into account both textual semantics and spatial layout. |
| Outcome: | The proposed model outperforms existing large language models on 14 out of 16 datasets and generalizes well to 4 out of 5 previously unseen datasets. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He
| Challenge: | Document images are characterized by higher resolutions, denser content, and more complex structural layouts. |
| Approach: | They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition. |
| Outcome: | The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead. |
CoCoLex: Confidence-guided Copy-based Decoding for Grounded Legal Text Generation (2025.acl-long)
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Santosh T.y.s.s, Youssef Tarek Elkhayat, Oana Ichim, Pranav Shetty, Dongsheng Wang, Zhiqiang Ma, Armineh Nourbakhsh, Xiaomo Liu
| Challenge: | LLMs can provide key benefits to the Legal domain, but their adoption has been hindered by their tendency to generate unfaithful, ungrounded, or hallucinatory outputs. |
| Approach: | They propose a Confidence-guided copy-based decoding strategy that dynamically interpolates the model produced vocabulary distribution with a distribution derived based on copying from the context. |
| Outcome: | The proposed method outperforms existing context-aware decoding methods on five legal benchmarks. |
Improving Large Language Models in Event Relation Logical Prediction (2024.acl-long)
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| Challenge: | Event relation extraction tasks require rigorous logical reasoning and semantic comprehension, a challenge for narrative understanding and reasoning. |
| Approach: | They propose three approaches to endow LLMs with event relation logic to generate more coherent answers across different scenarios. |
| Outcome: | The proposed approach improves on a set of ERE tasks and provides insights for future work. |