Papers by Yunyao Zhang
Doc-React: Multi-page Heterogeneous Document Question-answering (2025.acl-short)
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Junda Wu, Yu Xia, Tong Yu, Xiang Chen, Sai Sree Harsha, Akash V Maharaj, Ruiyi Zhang, Victor Bursztyn, Sungchul Kim, Ryan A. Rossi, Julian McAuley, Yunyao Li, Ritwik Sinha
| Challenge: | Existing methods for integrating information across multiple modalities are suboptimal for multi-page, multimodal documents. |
| Approach: | They propose an adaptive iterative framework that balances information gain and uncertainty reduction at each step. |
| Outcome: | The proposed framework captures relevant multimodal content and achieves strong performance on complex QA tasks. |
StorySparkQA: Expert-Annotated QA Pairs with Real-World Knowledge for Children’s Story-Based Learning (2024.emnlp-main)
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Jiaju Chen, Yuxuan Lu, Shao Zhang, Bingsheng Yao, Yuanzhe Dong, Ying Xu, Yunyao Li, Qianwen Wang, Dakuo Wang, Yuling Sun
| Challenge: | Existing story reading systems fail to capture the nuances of how education experts think when conducting interactive story reading activities. |
| Approach: | They propose to use existing question-answering (QA) datasets to capture experts' annotations and thinking process to construct a story-based annotation framework. |
| Outcome: | The proposed framework captures experts’ annotations and thinking process and can be used to generate 5, 868 expert-annotated QA pairs with real-world knowledge. |
Logical Phase Transitions: Understanding Collapse in LLM Logical Reasoning (2026.acl-long)
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| Challenge: | Symbolic logical reasoning is a critical yet underexplored capability of large language models (LLMs). |
| Approach: | They propose a framework that aligns natural language with logical symbols to establish a shared representation and reshapes training dynamics around phase-transition boundaries to progressively strengthen reasoning at increasing logical depths. |
| Outcome: | The proposed framework mitigates logical reasoning collapse at high complexity while improving generalization to unseen logical compositions. |
Semantic-Aware Logical Reasoning via a Semiotic Framework (2026.acl-long)
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Yunyao Zhang, Xinglang Zhang, Junxi Sheng, Wenbing Li, Junqing Yu, Yi-Ping Phoebe Chen, Wei Yang, Zikai Song
| Challenge: | Existing studies largely overlook the interplay between logical complexity and semantic complexity, limiting their robustness under abstract propositions, ambiguous contexts, and conflicting stances. |
| Approach: | They propose a semiotic-square-guided framework that integrates automated deduction with reflective verification to manage logical complexity across deeper reasoning chains. |
| Outcome: | The proposed framework achieves state-of-the-art performance on RepublicQA with 6.25% average gain, and generalizes well to four mainstream logical reasoning benchmarks with an additional 7.05% improvement. |
Label Definitions Improve Semantic Role Labeling (2022.naacl-main)
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| Challenge: | Existing work on semantic role labeling treats symbolic labels as symbolic . labeled data is costly and often lacking in many tasks, domains, and languages. |
| Approach: | They propose to retrieve and leverage semantic role labels from annotation guidelines . argument classification is at the core of Semantic Role Labeling . |
| Outcome: | The proposed model achieves state-of-the-art on a CoNLL09 dataset injected with label definitions given the predicate senses. |
MuRAR: A Simple and Effective Multimodal Retrieval and Answer Refinement Framework for Multimodal Question Answering (2025.coling-demos)
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| Challenge: | Recent advances in retrieval-augmented generation have demonstrated impressive performance on the question-answering task. |
| Approach: | They propose a retrieval-augmented generation framework that generates an initial text answer and retrieves multimodal data relevant to the snippets of the initial text. |
| Outcome: | The proposed framework can be easily integrated into an enterprise chatbot to produce multimodal answers with minimal modifications. |
GA-S3: Comprehensive Social Network Simulation with Group Agents (2025.findings-acl)
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| Challenge: | Existing social network simulations focus on discrete events or system dynamics instead of elucidating underlying mechanisms or causal relationships. |
| Approach: | They propose a Social network simulation system that leverages newly designed Group Agents to make intelligent decisions regarding various online events. |
| Outcome: | The proposed system can make intelligent decisions regarding online events at a manageable cost. |
Small but Mighty: New Benchmarks for Split and Rephrase (2020.emnlp-main)
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| Challenge: | Split and Rephrase is a text simplification task that requires a strong evaluation benchmark and metric . despite its relatively new nature, the benchmark dataset contains easily exploitable syntactic cues . |
| Approach: | They propose to use crowdsourced datasets to evaluate split and rephrase models . they find that the widely used benchmark dataset universally contains exploitable syntactic cues . |
| Outcome: | The proposed model performs better than the state-of-the-art model, the authors say . they show that the datasets contain significantly more diverse syntax . |