Bowen Qin
National University of Singapore (NUS), Singapore. eyuansu71@gmail.com
I am a 1st-year Ph.D. student at the National University of Singapore (NUS), advised by Prof. Yao Lu.
Previously, I was a researcher at the Beijing Academy of Artificial Intelligence (BAAI), specializing in the evaluation, alignment, and code intelligence of large language models (LLMs). Previously, I obtained my master’s degree with top honors from the Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), under the guidance of Prof. Min Yang in 2023. I was a research intern at Alibaba DAMO Academy mentored by Binyuan Hui.
I am a member of the BIRD team, which drives the development of text-to-SQL for real-world database applications.
Throughout my academic journey, I collaborated with many talented researchers, including: Jinyang Li, Duanyu Feng, Binyuan Hui and Yequan Wang.
news
| Jul 01, 2024 | Our team secured 7th place out of over 100 global competitors in the AI Safety and Security Challenge hosted by AI Singapore (AISG) and the National University of Singapore (NUS), and was invited to attend the Singapore International Cyber Week (SICW) 2024. |
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recent publications
- arXivAgent Retrieval Bench: Evaluating Repository Context Retrieval for Coding AgentsarXiv preprint arXiv:2607.24882, Jul 2026
- arXiv
- arXivLaoBench: A Large-Scale Multidimensional Lao Benchmark for Large Language ModelsarXiv preprint arXiv:2511.11334, Nov 2025
- arXivBeyond Multiple Choice: Verifiable OpenQA for Robust Vision-Language RFTarXiv preprint arXiv:2511.17405, Nov 2025
- arXivBIRD-INTERACT: Re-imagining Text-to-SQL Evaluation for Large Language Models via Lens of Dynamic InteractionsarXiv preprint arXiv:2510.05318, Oct 2025
selected publications
- arXivFlagEval Findings Report: A Preliminary Evaluation of Large Reasoning Models on Automatically Verifiable Textual and Visual QuestionsarXiv preprint arXiv:2509.17177, Sep 2025
- arXivTowards analyzing and understanding the limitations of DPO: A theoretical perspectivearXiv preprint arXiv:2404.04626, 2024
- ACLBefore generation, align it! A novel and effective strategy for mitigating hallucinations in text-to-sql generationIn Association for Computational Linguistics (ACL Findings), 2024
- AAAIGraphix-t5: Mixing pre-trained transformers with graph-aware layers for text-to-sql parsingIn Proceedings of the AAAI Conference on Artificial Intelligence, 2023
- NeurIPSCan LLM already serve as a database interface? A big bench for large-scale database grounded text-to-sqlsIn Advances in Neural Information Processing Systems, 2023
- ACLS^2 SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL ParsersIn Association for Computational Linguistics (ACL Findings), 2022
- COLINGSUN: Exploring intrinsic uncertainties in text-to-SQL parsersIn Proceedings of the 29th International Conference on Computational Linguistics, 2022
- SIGKDDProton: Probing schema linking information from pre-trained language models for text-to-sql parsingIn Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022
- arXivA survey on text-to-sql parsing: Concepts, methods, and future directionsarXiv preprint arXiv:2208.13629, 2022