--- title: "Fei Mi · Huawei Technologies — author profile and papers" description: "Fei Mi, Huawei Technologies: 3 papers on Elamur Research (2023–2024), h-index 22 — affiliations, topics, co-authors and who cites them in…" canonical: "https://research.elamur.ai/free/en/authors/fei-mi/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/fei-mi/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Fei Mi # Fei Mi Fei Mi has 3 papers in our library, in 2023–2024, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/), [Bias, fairness & safety](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/); often with [Liangyou Li](https://md.research.elamur.ai/free/en/authors/liangyou-li/), [Lifeng Shang](https://md.research.elamur.ai/free/en/authors/lifeng-shang/), [Qun Liu](https://md.research.elamur.ai/free/en/authors/qun-liu/). 3 papers in our library · 2023–2024 [OpenAlex](https://openalex.org/A5012014905) · [ORCID](https://orcid.org/0000-0001-6358-9922) · [**h-index 22**](https://openalex.org/authors/A5012014905) · [100 works](https://openalex.org/works?filter=author.id:A5012014905) · [1,404 citations](https://openalex.org/works?filter=author.id:A5012014905&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 3 works in our library](https://md.research.elamur.ai/free/en/authors/fei-mi/#papers) · [🔗 Cited 324 times in our library](https://md.research.elamur.ai/free/en/authors/fei-mi/cited-by/) · [5 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #3761](https://md.research.elamur.ai/free/en/paper-stars/page/38/#star-3761) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Fei+Mi) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Fei+Mi) · [dblp](https://dblp.org/search?q=Fei+Mi) · [ACL Anthology](https://aclanthology.org/search/?q=Fei+Mi) ## Affiliations [🏢**Huawei Technologies** Shenzhen · China 2021–2026 · 45 papers*current*](https://md.research.elamur.ai/free/en/companies/huawei-technologies/) · [🎓**Kunming Medical University** Kunming · China 2018–2025 · 15 papersEmployment since 2018 · School of Public Health](https://ror.org/038c3w259) · [🎓**École Polytechnique Fédérale de Lausanne** Lausanne · Switzerland 2016–2021 · 12 papers](https://md.research.elamur.ai/free/en/universities/ecole-polytechnique-federale-de-lausanne/) · [🎓**Yunnan University** Kunming · China 2013–2021 · 5 papersEducation](https://md.research.elamur.ai/free/en/universities/yunnan-university/) · [🎓**Chinese University of Hong Kong** Hong Kong · Hong Kong 2023–2024 · 3 papers](https://md.research.elamur.ai/free/en/universities/chinese-university-of-hong-kong/) · [🏛️**Guizhou Center for Disease Control and Prevention** Yunyan · China 2016–2020 · 2 papers](https://ror.org/009j0tv77) 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 4 The field of science this work belongs to (OpenAlex fields).[Artificial Intelligence · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Arts and Humanities · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Language and Linguistics · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) 🧭 Topics 3 Research subjects and languages the work is about.[Language models & pretraining · 3 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Bias, fairness & safety · 1 work](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) · [Evaluation & benchmarks · 1 work](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) ## Works by year ### 2020s [**2024** 2 papers Fei Mi's papers of 2024](https://md.research.elamur.ai/free/en/authors/fei-mi/year/2024/) · [**2023** 1 paper Aligning Large Language Models with Human: A Survey](https://md.research.elamur.ai/free/en/papers/wang-2023-aligning-large-language-models-human/) ## Papers The most cited first (OpenAlex and our library). 1. **[Aligning Large Language Models with Human: A Survey](https://md.research.elamur.ai/free/en/papers/wang-2023-aligning-large-language-models-human/)** *(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](https://md.research.elamur.ai/free/en/papers/zhang-2024-defending-large-language-models-against/)** *(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](https://md.research.elamur.ai/free/en/papers/jiang-2024-followbench-multi-level-fine-grained/)** *(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 Survey arXiv:2307.12966**Yufei Wang, Wanjun Zhong, Liangyou Li, et al.*2023 · 199 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-aligning-large-language-models-human/) 2. **[Defending Large Language Models Against Jailbreaking Attacks Through Goal Prioritization](https://md.research.elamur.ai/free/en/papers/zhang-2024-defending-large-language-models-against/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 Models](https://md.research.elamur.ai/free/en/papers/jiang-2024-followbench-multi-level-fine-grained/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuxin Jiang, Yufei Wang, Xingshan Zeng, et al.*2024 · 85 citations* 4. **[Compilable Neural Code Generation with Compiler Feedback](https://aclanthology.org/2022.findings-acl.2/) Findings of the Association for Computational Linguistics: ACL 2022 · original ↗ · no summary yet**Xin Wang et al.*2022 · 63 citations* 5. **[Pangu Embedded: An Efficient Dual-system LLM Reasoner with Metacognition](https://arxiv.org/abs/2505.22375) arXiv:2505.22375 · original ↗ · no summary yet**Hanting Chen et al.*2025 · 13 citations* 6. **[How Should We Enhance the Safety of Large Reasoning Models: An Empirical Study](https://aclanthology.org/2026.acl-long.936/) Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · original ↗ · no summary yet**Zhexin Zhang et al.*2026 · 5 citations* 7. **[Self-Error-Instruct: Generalizing from Errors for LLMs Mathematical Reasoning](https://aclanthology.org/2025.acl-long.417/) Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · original ↗ · no summary yet**Erxin Yu et al.*2025 · 5 citations* 8. **[Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity](https://arxiv.org/abs/2505.21411) arXiv:2505.21411 · original ↗ · no summary yet**Yehui Tang et al.*2025 · 4 citations* ## Co-authors here 12 of 13 co-authors with a page, the most papers together first. - [Liangyou Li](https://md.research.elamur.ai/free/en/authors/liangyou-li/) ×2 - [Lifeng Shang](https://md.research.elamur.ai/free/en/authors/lifeng-shang/) ×2 - [Qun Liu](https://md.research.elamur.ai/free/en/authors/qun-liu/) ×2 - [Wanjun Zhong](https://md.research.elamur.ai/free/en/authors/wanjun-zhong/) ×2 - [Xin Jiang](https://md.research.elamur.ai/free/en/authors/xin-jiang/) ×2 - [Xingshan Zeng](https://md.research.elamur.ai/free/en/authors/xingshan-zeng/) ×2 - [Yufei Wang](https://md.research.elamur.ai/free/en/authors/yufei-wang/) ×2 - [Hongning Wang](https://md.research.elamur.ai/free/en/authors/hongning-wang/) - [Minlie Huang](https://md.research.elamur.ai/free/en/authors/minlie-huang/) - [Pei Ke](https://md.research.elamur.ai/free/en/authors/pei-ke/) - [Wei Wang](https://md.research.elamur.ai/free/en/authors/wei-wang/) - [Yuxin Jiang](https://md.research.elamur.ai/free/en/authors/yuxin-jiang/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/) ×4: [ACL 2024](https://md.research.elamur.ai/free/en/venues/acl/2024/) ×2 - Findings of the Association for Computational Linguistics: ACL 2022 ## 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 →](https://md.research.elamur.ai/free/en/authors/fei-mi/cited-by/). Updated 24 October 2026