Papers by Xiaodong Gu
Continuous Decomposition of Granularity for Neural Paraphrase Generation (2022.coling-1)
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| Challenge: | Prior work has shown that decomposing sentences at different levels of granularity has improved paragraph generation. |
| Approach: | They propose a model for continuous decomposing granularity for neural paraphrase generation that incorporates granules into attention. |
| Outcome: | The proposed model outperforms baseline models on Quora question pairs and Twitter URLs on two benchmarks. |
EvoHyper: Evolving Hypergraph Topologies for Unified Collaboration in Multi-Agent Communication (2026.findings-acl)
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Heng Zhang, Yihao Zhong, Lubin Gan, Zhihe Chen, Jiajun Wu, Yuling Shi, Xiaodong Gu, Hao Zhang, Haochen You, Jin Huang
| Challenge: | Existing methods for multi-agent collaboration use a fixed communication graph and manage collaboration structure and shared memory in separate modules. |
| Approach: | They propose a framework that uses an evolving hypergraph topology for multi-agent collaboration. |
| Outcome: | The proposed framework achieves 3.2% to 7.8% accuracy gains over state-of-the-art methods and efficient, reducing token consumption by up to 23.5%. |
SWE-QA: Can Language Models Answer Repository-level Code Questions? (2026.findings-acl)
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| Challenge: | Existing benchmarks for understanding and reasoning about entire soft-ware repositories focus on small, self-contained code snippets. |
| Approach: | They propose a repository-level code question answering benchmark to facilitate research on automated QA systems in real-world repositories. |
| Outcome: | The proposed benchmarks are designed to facilitate research on automated QA systems in real-world repositories. |
GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of Thoughts (2026.findings-acl)
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| Challenge: | Existing routing strategies rely on local token probabilities or post-hoc verification, introducing significant inference overhead. |
| Approach: | They propose a step-wise collaboration framework that generates only the first token of each reasoning step and routes it to a larger model only when initial token entropy exceeds a threshold. |
| Outcome: | The proposed approach reduces inference latency while preserving accuracy. |
Transplant Then Regenerate: A New Paradigm for Text Data Augmentation (2025.emnlp-main)
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| Challenge: | Data augmentation is a critical technique in deep learning. |
| Approach: | They propose a novel text augmentation paradigm leveraging large language models . they incorporate seed text into a context expanded by LLM and ask it to regenerate a variant based on the expanded context. |
| Outcome: | The proposed model generates high-quality and diverse augmented text with a transplant-then-regenerate approach. |
ShredBench: Evaluating the Semantic Reasoning Capabilities of Multimodal LLMs in Document Reconstruction (2026.findings-acl)
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Zichun Guo, Yuling Shi, Wenhao Zeng, Chao Hu, Haotian Lin, Terry Yue Zhuo, Jiawei Chen, Xiaodong Gu, Wenping Ma
| Challenge: | Empirical evaluations on state-of-the-art MLLMs reveal a significant performance gap . ML models lack the fine-grained cross-modal reasoning required to bridge visual discontinuities. |
| Approach: | They propose a benchmark that renders fragmented documents directly from Markdown to facilitate evaluation of VRDU tasks. |
| Outcome: | The proposed benchmark renders fragmented documents directly from Markdown. |
Building Joint Relationship Attention Network for Image-Text Generation (2022.coling-1)
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| Challenge: | et al., 2017) focus on visual features individually, while ignoring relationship information among image features that provides important guidance for generating sentences. |
| Approach: | They propose a joint relationship attention network that explores the relationships among image features. |
| Outcome: | The proposed method achieves state-of-the-art performance on large-scale datasets and on Flickr30k datasets. |
LastingBench: Defend Benchmarks Against Knowledge Leakage (2025.findings-emnlp)
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| Challenge: | Existing methods to detect and safeguard LLMs against knowledge leakage fail to address the long-term challenge of mitigating it. |
| Approach: | They propose a method to reinforce and safeguard existing benchmarks against knowledge leakage by perturbation-based detection and counterfactual rewriting to disrupt memorization while preserving original intent. |
| Outcome: | The proposed method reduces memorization effects in long-context QA benchmarks, providing a more accurate assessment of model reasoning and generalization abilities. |