I work as a Research Scientist at Baidu Search now in Beijing, collaborating with Dr. Dawei Yin and Dr. Shuaiqiang Wang. I received my Ph.D. degree from the Institute of Software, Chinese Academy of Sciences (ISCAS) under the supervision of Professor Le Sun and Professor Xianpei Han. My recent research interests include large language model, information retrieval, super-alignment, etc. I published over 10 papers in leading AI conferences. And my work has received the Outstanding Paper Award of EMNLP 2023.
🔔Recruitment of Research Interns🔔 I’m looking for enthusiastic and self-motivated Ph.D. students as interns in Baidu Search. If you are interested in Large Language Model and Artificial General Intelligence, please send your resume to yanlingyong#baidu.com(#➡️at).
Recent Research Interests
- Large Language Model and Generative Retrieval
- Generative Agent and Tool Learning
- Model Alignment and Reinforcement Learning
Selected Publications
- Junda Zhu*, Lingyong Yan*(co-first author), Haibo Shi, Dawei Yin, Lei Sha. 2024. ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator. Accepted to EMNLP 2024.
- Weiwei Sun, Zhengliang Shi, Jiulong Wu, Lingyong Yan, Xinyu Ma, Yiding Liu, Min Cao, Dawei Yin, Zhaochun Ren. 2024. MINT: A Benchmark for Evaluating Instructed Information Retrieval. Accepted to EMNLP 2024.
- Yougang Lyu, Lingyong Yan, Shuaiqiang Wang, Haibo Shi, Dawei Yin, Pengjie Ren, Zhumin Chen, Maarten de Rijke and Zhaochun Ren. 2024. KnowTuning: Knowledge-aware Fine-tuning for Large Language Models. Accepted to EMNLP 2024.
- Zhengliang Shi, Shen Gao, Xiuyi Chen, Lingyong Yan, Haibo Shi, Dawei Yin, Zhumin Chen, Pengjie Ren, Suzan Verberne and Zhaochun Ren. 2024. Learning to Use Tools via Cooperative and Interactive Agents. Accepted to EMNLP 2024 Findings.
- Bolei He, Nuo Chen, Xinran He, Lingyong Yan, Zhenkai Wei, Jinchang Luo, Zhen-Hua Ling. 2024. Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation. Accepted to EMNLP 2024 Findings.
- Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Chong Meng, Shuaiqiang Wang, Zhicong Cheng, Zhaochun Ren and Dawei Yin. 2024. Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method. In proceedings of Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL 2024).
- Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Shuaiqiang Wang, Chong Meng, Zhicong Cheng, Zhaochun Ren and Dawei Yin. 2024. Improving the Robustness of Large Language Models via Consistency Alignment. In proceedings of Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024).
- Weiwei Sun, Lingyong Yan, Xinyu Ma, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, Zhaochun Ren. 2023. Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents. In proceedings of Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023). Outstanding Paper
- Yukun Zhao, Lingyong Yan*(co-first author), Weiwei Sun, Chong Meng, Shuaiqiang Wang, Zhicong Cheng, Zhaochun Ren, Dawei Yin. 2023. DiQAD: A Benchmark Dataset for Open-domain Dialogue Quality Assessment. In proceedings of Findings of the Association for Computational Linguistics: EMNLP 2023.
- Weiwei Sun, Lingyong Yan, Zheng Chen, Shuaiqiang Wang, Haichao Zhu, Pengjie Ren, Zhumin Chen, Dawei Yin, Maarten de Rijke, Zhaochun Ren. 2023. Learning to Tokenize for Generative Retrieval. In proceedings of Advances in Neural Information Processing Systems (NeurIPS 2023).
- Lingyong Yan, Xianpei Han and Le Sun. Progressive Adversarial Learning for Bootstrapping: A Case Study on Entity Set Expansion. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP2021). (paper, code)
Services
Program Committee Member
- Area Chair/Action Editor: ACL ARR
- Reviewer: ACL, EMNLP, NAACL, AAAI, etc.
Journal Reviewer
- Transactions on Knowledge and Data Engineering