--- title: "Bei Yu — author profile and papers · Evaluation & benchmarks" description: "Bei Yu, Chinese University of Hong Kong: 3 papers on Elamur Research (2026), h-index 0 — affiliations, topics, co-authors and who cites…" canonical: "https://research.elamur.ai/free/en/authors/bei-yu/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/bei-yu/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Bei Yu # Bei Yu Bei Yu has 3 papers in our library, in 2026, most of them on [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/); often with [Haoru Tan](https://md.research.elamur.ai/free/en/authors/haoru-tan/), [Hui-Ling Zhen](https://md.research.elamur.ai/free/en/authors/hui-ling-zhen/), [Jiaya Jia](https://md.research.elamur.ai/free/en/authors/jiaya-jia/). 3 papers in our library · 2026 [OpenAlex](https://openalex.org/A5139848179) · [**h-index 0**](https://openalex.org/authors/A5139848179) · [2 works](https://openalex.org/works?filter=author.id:A5139848179) · [0 citations](https://openalex.org/works?filter=author.id:A5139848179&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 3 works in our library](https://md.research.elamur.ai/free/en/authors/bei-yu/#papers) · 🔗 Cited 1 time in our library · [⭐ Paper Stars ranking: #12729](https://md.research.elamur.ai/free/en/paper-stars/page/128/#star-12729) 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=Bei+Yu) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Bei+Yu) · [dblp](https://dblp.org/search?q=Bei+Yu) · [ACL Anthology](https://aclanthology.org/search/?q=Bei+Yu) ## Affiliations [🎓**Chinese University of Hong Kong** Hong Kong · China 2026 · 2 papers*current*](https://md.research.elamur.ai/free/en/universities/chinese-university-of-hong-kong/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 5 The field of science this work belongs to (OpenAlex fields).[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/) · [Artificial Intelligence · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) 🧭 Topics 1 Research subjects and languages the work is about.[Evaluation & benchmarks · 1 work](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) ## Works by year ### 2020s [**2026** 3 papers](https://md.research.elamur.ai/free/en/authors/bei-yu/year/2026/) ## Papers The most cited first (OpenAlex and our library). 1. **[Analytical FFN-to-MoE Restructuring via Activation Pattern Analysis](https://md.research.elamur.ai/free/en/papers/pei-2026-analytical-ffn-moe-restructuring-activation/)** *(2026 · 1 citation)* Most inference compute in large language models goes to the feed-forward (FFN) blocks. Converting a dense model into a Mixture-of-Experts (MoE) can cut that cost, but earlier conversion methods needed continual training on billions of tokens. Pei et al. instead restructure FFNs after training, in a single analytical… 2. **[PROBE: PROcess-Based BEnchmark for Hallucination Detection](https://md.research.elamur.ai/free/en/papers/zhang-2026-probe-process-based-benchmark-hallucination/)** *(2026)* LLM-as-a-judge setups usually ask a model for a single verdict on whether an output stays faithful to its source. 3. **[TRAC: Teacher-Guided Token Reward with Adaptive Calibration for Robust Policy Optimization](https://md.research.elamur.ai/free/en/papers/wu-2026-trac-teacher-guided-token-reward/)** *(2026)* Reinforcement learning for LLM reasoning usually depends on a single correctness signal at the end of a solution. ## Co-authors here 12 of 15 co-authors with a page, the most papers together first. - [Haoru Tan](https://md.research.elamur.ai/free/en/authors/haoru-tan/) - [Hui-Ling Zhen](https://md.research.elamur.ai/free/en/authors/hui-ling-zhen/) - [Jiaya Jia](https://md.research.elamur.ai/free/en/authors/jiaya-jia/) - [Mingxuan Yuan](https://md.research.elamur.ai/free/en/authors/mingxuan-yuan/) - [Pavlo Molchanov](https://md.research.elamur.ai/free/en/authors/pavlo-molchanov/) - [Peter Belcak](https://md.research.elamur.ai/free/en/authors/peter-belcak/) - [Shaona Ghosh](https://md.research.elamur.ai/free/en/authors/shaona-ghosh/) - [Shizhe Diao](https://md.research.elamur.ai/free/en/authors/shizhe-diao/) - [Sinno Jialin Pan](https://md.research.elamur.ai/free/en/authors/sinno-jialin-pan/) - [Sitong Wu](https://md.research.elamur.ai/free/en/authors/sitong-wu/) - [Wenhu Zhang](https://md.research.elamur.ai/free/en/authors/wenhu-zhang/) - [Xianzhi Yu](https://md.research.elamur.ai/free/en/authors/xianzhi-yu/) ## 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/) ×2: [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/) ×2 - [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/): [Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/) ## Cited by Papers of our library cite their works 1 time (of the 149,439 papers whose bibliographies we read). Updated 2 November 2026