Papers by Shuang Cao
A Dialogue-based Information Extraction System for Medical Insurance Assessment (2021.findings-acl)
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Shuang Peng, Mengdi Zhou, Minghui Yang, Haitao Mi, Shaosheng Cao, Zujie Wen, Teng Xu, Hongbin Wang, Lei Liu
| Challenge: | a new system that integrates advanced NLP technologies for medical insurance assessment is proposed . the average time cost of the procedure is reduced from 55 minutes to 35 minutes . |
| Approach: | They propose a dialogue-based information extraction system that integrates advanced NLP technologies for medical insurance assessment. |
| Outcome: | The proposed system reduces the time cost of the procedure from 55 minutes to 35 minutes and saves 30% human resources cost compared with the previous offline procedure. |
AMO-Bench: Large Language Models Still Struggle in High School Math Competitions (2026.findings-acl)
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Junlin Liu, Shengnan An, Shuang Zhou, Dan Ma, Yehao Lin, Xinxuan Lv, Xuanlin Wang, Xiaoyu Li, Ziwen Wang, Xuezhi Cao, Xunliang Cai
| Challenge: | Existing benchmarks for mathematical reasoning are becoming less effective due to performance saturation. |
| Approach: | They propose to use a mathematical reasoning benchmark with Olympiad difficulty to evaluate top-tier LLMs. |
| Outcome: | The proposed benchmarks are cross-validated by experts to meet IMO difficulty standards and entirely original problems to prevent performance leakages from data memorization. |
MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains (2025.findings-naacl)
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Guoli Yin, Haoping Bai, Shuang Ma, Feng Nan, Yanchao Sun, Zhaoyang Xu, Shen Ma, Jiarui Lu, Xiang Kong, Aonan Zhang, Dian Ang Yap, Yizhe Zhang, Karsten Ahnert, Vik Kamath, Mathias Berglund, Dominic Walsh, Tobias Gindele, Juergen Wiest, Zhengfeng Lai, Xiaoming Simon Wang, Jiulong Shan, Meng Cao, Ruoming Pang, Zirui Wang
| Challenge: | Existing benchmarks focus on specific application scenarios, emphasizing task completion but failing to dissect the underlying skills that drive these outcomes. |
| Approach: | They propose a Massive Multitask Agent Understanding benchmark that evaluates LLMs across five domains and offline tasks. |
| Outcome: | The Massive Multitask Agent Understanding (MMAU) benchmark evaluates models across five domains including Tool-use, Directed Acyclic Graph (DAG) QA, Data Science and Machine Learning coding, Contest-level programming and Mathematics. |
From Trajectories to Graphs: Contract-Checked Editing for Verifier-Guided LLM Reasoning (2026.acl-long)
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| Challenge: | Existing methods for inference-time search refine single trajectories and lack a reliable mechanism for composing partial solutions across candidates. |
| Approach: | a new method uses a gate-based algorithm to validate a nontrivial edit before invoking the verifier. |
| Outcome: | a new method improves verifier-runnable recombination and accuracy over existing methods . it outperforms execution-guided beam search on Spider and humanEval-MF on MCTS . a contract-checked graph editing improves recompilation and recombines partial solutions . |
DISK: Domain-constrained Instance Sketch for Math Word Problem Generation (2022.coling-1)
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| Challenge: | Existing methods for generating MWP text from equations are inflexible and require pre-defined templates. |
| Approach: | They propose a neural model which generates MWPs from equations by constructing a Quantity Cell Graph from the retrieved MWp instance and reasoning over it. |
| Outcome: | The proposed model performs impressively on educational MWP set and on human evaluation metrics. |