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Fei Mi

Fei Mi has 3 papers in our library, in 2023–2024, most of them on Language models & pretraining, Evaluation & benchmarks, Bias, fairness & safety; often with Liangyou Li, Lifeng Shang, Qun Liu.

3 papers in our library · 2023–2024

OpenAlex · ORCID · h-index 22 · 100 works · 1,404 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 324 times in our library · 5 more in our library without a page yet · ⭐ Paper Stars ranking: #3761

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢Huawei TechnologiesShenzhen · China2021–2026 · 45 paperscurrent🎓Kunming Medical UniversityKunming · China2018–2025 · 15 papersEmployment since 2018 · School of Public Health🎓École Polytechnique Fédérale de LausanneLausanne · Switzerland2016–2021 · 12 papers🎓Yunnan UniversityKunming · China2013–2021 · 5 papersEducation🎓Chinese University of Hong KongHong Kong · Hong Kong2023–2024 · 3 papers🏛️Guizhou Center for Disease Control and PreventionYunyan · China2016–2020 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 3 worksBias, fairness & safety · 1 workEvaluation & benchmarks · 1 work

 Works by year

2020s

20242 papersFei Mi's papers of 202420231 paperAligning Large Language Models with Human: A Survey

 Papers

The most cited first (OpenAlex and our library).

  1. Aligning Large Language Models with Human: A Survey (2023 · 199 citations)
    Foundation LLMs are pre-trained only to predict the next token. As a result, they often misread user requests, produce biased output, or invent facts. This survey from Huawei Noah's Ark Lab maps research on bringing such models closer to human expectations and organizes it into a three-part taxonomy.
  2. Defending Large Language Models Against Jailbreaking Attacks Through Goal Prioritization (2024 · 112 citations)
    The paper argues that jailbreaking attacks on LLMs succeed because models are tuned for two competing objectives, helpfulness and safety, without being told which one wins when they clash.
  3. FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models (2024 · 85 citations)
    FollowBench addresses a gap in how LLMs are evaluated. Most existing benchmarks judge the overall quality of a response but do not check whether the model actually obeyed the explicit requirements in a prompt. The authors built a benchmark of 820 curated instructions drawn from over 50 NLP tasks. Across these…

All works

8 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Aligning Large Language Models with Human: A SurveyarXiv:2307.12966Yufei Wang, Wanjun Zhong, Liangyou Li, et al.2023 · 199 citations
  2. Defending Large Language Models Against Jailbreaking Attacks Through Goal PrioritizationAnnual Meeting of the Association for Computational Linguistics (ACL)Zhexin Zhang, Junxiao Yang, Pei Ke, et al.2024 · 112 citations
  3. FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Yuxin Jiang, Yufei Wang, Xingshan Zeng, et al.2024 · 85 citations
  4. Compilable Neural Code Generation with Compiler FeedbackFindings of the Association for Computational Linguistics: ACL 2022 · original ↗ · no summary yetXin Wang et al.2022 · 63 citations
  5. Pangu Embedded: An Efficient Dual-system LLM Reasoner with MetacognitionarXiv:2505.22375 · original ↗ · no summary yetHanting Chen et al.2025 · 13 citations
  6. How Should We Enhance the Safety of Large Reasoning Models: An Empirical StudyProceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · original ↗ · no summary yetZhexin Zhang et al.2026 · 5 citations
  7. Self-Error-Instruct: Generalizing from Errors for LLMs Mathematical ReasoningProceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · original ↗ · no summary yetErxin Yu et al.2025 · 5 citations
  8. Pangu Pro MoE: Mixture of Grouped Experts for Efficient SparsityarXiv:2505.21411 · original ↗ · no summary yetYehui Tang et al.2025 · 4 citations

 Co-authors here

12 of 13 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 324 times (of the 141,840 papers whose bibliographies we read); 30 citing papers with a page here →.

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