Xiaodong Gu
Xiaodong Gu has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining, Question answering, Code & programming languages; often with Yuling Shi, Beijun Shen, Min Wang.
OpenAlex · ORCID · Wikidata · h-index 19 · 64 works · 2,435 citations OpenAlex, 2026-10-14
📄 3 works in our library · 🔗 Cited 41 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #8859
Affiliations
Fields, topics and concepts
📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksInformation Systems · 2 worksArts and Humanities · 2 worksComputer Science Applications · 1 workSoftware · 1 workArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work
🧭 Topics 3Research subjects and languages the work is about.Code & programming languages · 1 workLanguage models & pretraining · 1 workQuestion answering · 1 work
Works by year
2020s
Papers
- From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging (2024 · 15 citations)
LLMs often write code that is almost right but fails tests because of small mistakes. Existing LLM-based repair loops usually look at the whole program at once, which makes bugs hard to locate. The authors propose MGDebugger, which needs no extra training and works in a zero-shot setting. First, an LLM restructures… - SWE-QA: Can Language Models Answer Repository-level Code Questions? (2026 · 7 citations)
Most code question-answering benchmarks, such as CoSQA and CodeQA, test models on short, isolated snippets. Real developer questions often require moving across files, following dependencies and understanding a project's architecture. SWE-QA is a benchmark built for this repository-level setting. - GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of Thoughts (2026 · 1 citation)
Large reasoning models produce long chains of thought, which makes inference slow and expensive. Collaborative inference addresses this by splitting work between a small and a large model. The hard part is deciding cheaply which model should handle each step. Existing step-level routers either generate a full step…
All works
- DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder ↗arXiv (Cornell University) · no summary yetXiaodong Gu et al.2018 · 132 citations
- From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical DebuggingarXiv:2410.01215Yuling Shi, Songsong Wang, Chengcheng Wan, et al.2024 · 15 citations
- SWE-QA: Can Language Models Answer Repository-level Code Questions?Findings of the Association for Computational LinguisticsWeihan Peng, Yuling Shi, Yuhang Wang, et al.2026 · 7 citations
- Continuous Decomposition of Granularity for Neural Paraphrase Generation ↗Proceedings of the 29th International Conference on Computational Linguistics · no summary yetXiaodong Gu et al.2022 · 2 citations
- GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of ThoughtsFindings of the Association for Computational LinguisticsWenhao Zeng, Xuteng Zhang, Yuling Shi, et al.2026 · 1 citation
- EvoHyper: Evolving Hypergraph Topologies for Unified Collaboration in Multi-Agent Communication ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetHeng Zhang et al.2026
Co-authors here
Venues
- Findings of the Association for Computational Linguistics ×2: Findings of the Association for Computational Linguistics 2026 ×2
- Findings of the Association for Computational Linguistics: ACL 2026
- Proceedings of the 29th International Conference on Computational Linguistics
Cited by
Updated
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