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Langming Liu

Langming Liu has 3 papers in our library, in 2026, most of them on Language models & pretraining; often with Bo Zheng, Haibin Chen, Shilei Liu.

3 papers in our library · 2026

OpenAlex · ORCID · Wikidata · h-index 4 · 26 works · 712 citations OpenAlex, 2026-10-14

📄 3 works in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #11830

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

🏢Alibaba GroupHangzhou · China2025–2026 · 18 paperscurrent🏢Alibaba GroupSan Mateo · United States2026 · 11 paperscurrent🎓City University of Hong KongHong Kong · China2023–2025 · 4 papersEducation until 2024

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 7 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 2 works

 Works by year

2020s

20263 papers

 Papers

The most cited first (OpenAlex and our library).

  1. PretrainRL: Alleviating Factuality Hallucination of Large Language Models at the Beginning (2026)
    The paper addresses factual hallucinations in LLMs, meaning confident statements that can be checked and shown false.
  2. Read As Human: Compressing Context via Parallelizable Close Reading and Skimming (2026)
    Long prompts, such as those produced by retrieval-augmented generation, make LLM inference slow because attention cost grows quadratically.
  3. SELECting over Tokens: Curating Pre-training Data at Scale via Token Classification (2026)
    Web-scraped pre-training corpora contain a lot of boilerplate and noise. Standard cleaning pipelines use document-level heuristics that can only keep or drop whole pages. Generative token-level refiners such as ProX-C are finer-grained but slow at scale and may hallucinate edits. The authors propose SelecT, which…

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. PretrainRL: Alleviating Factuality Hallucination of Large Language Models at the BeginningFindings of the Association for Computational LinguisticsLangming Liu, Kangtao Lv, Haibin Chen, et al.2026
  2. Read As Human: Compressing Context via Parallelizable Close Reading and SkimmingAnnual Meeting of the Association for Computational Linguistics (ACL)Jiwei Tang, Shilei Liu, Zhicheng Zhang, et al.2026
  3. SELECting over Tokens: Curating Pre-training Data at Scale via Token ClassificationAnnual Meeting of the Association for Computational Linguistics (ACL)Xin Tong, Weidong Zhang, Jiaang Li, et al.2026
  4. CoMeT: Collaborative Memory Transformer for Efficient Long Context Modeling ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetRunsong Zhao et al.2026

 Co-authors here

12 of 14 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.

Updated

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