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Bowen Shi

Bowen Shi has 3 papers in our library, in 2023–2026, most of them on Language models & pretraining; often with Alexei Baevski, Ali Elkahky, Andros Tjandra.

3 papers in our library · 2023–2026

OpenAlex · h-index 0 · 2 works · 0 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 229 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #4671

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

🎓Xiamen UniversityXiamen · China2026 · 1 papercurrent

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 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksSignal Processing · 1 workComputer Vision and Pattern Recognition · 2 worksComputational Theory and Mathematics · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 1 work

 Works by year

2020s

20261 paper20251 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Scaling Speech Technology to 1,000+ Languages (2023 · 334 citations)
    Most speech technology today handles only about a hundred of the world's 7,000+ languages. This leaves most speakers without tools, and the gap may hasten language loss. The Massively Multilingual Speech (MMS) project from Meta AI widens coverage by roughly an order of magnitude or more. The authors collected…
  2. MDCure: A Scalable Pipeline for Multi-Document Instruction-Following (2025 · 11 citations)
    Large language models can now read very long inputs, yet they still handle sets of related documents poorly.
  3. Profiling-Free Mixed-Precision Quantization for MoE LLMs via Fuzzy Rule Interpolation (2026)
    Compressing Mixture-of-Experts LLMs with mixed-precision quantization depends on knowing how sensitive each expert, and each linear sub-layer inside it (gate, up, down), is to lower bit-widths.

All works

4 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. Scaling Speech Technology to 1,000+ LanguagesarXiv:2305.13516Vineel Pratap, Andros Tjandra, Bowen Shi, et al.2023 · 334 citations
  2. MDCure: A Scalable Pipeline for Multi-Document Instruction-FollowingAnnual Meeting of the Association for Computational Linguistics (ACL)Gabrielle Kaili-May Liu, Bowen Shi, Avi Caciularu, et al.2025 · 11 citations
  3. Profiling-Free Mixed-Precision Quantization for MoE LLMs via Fuzzy Rule InterpolationAnnual Meeting of the Association for Computational Linguistics (ACL)Huachen Qi, Ruiyu Zhuo, Bowen Shi, et al.2026
  4. CT-FineBench: A Diagnostic Fidelity Benchmark for Fine-Grained Evaluation of CT Report Generation ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetRuifeng Yuan et al.2026

 Co-authors here

12 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 229 times (of the 149,439 papers whose bibliographies we read); 60 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/bowen-shi/