🧑‍🔬 Shilei Liu

🏛️ Affiliations

🏢Alibaba GroupHangzhou · China2023–2026 · 21 papersEmploymentcurrent🏢Alibaba GroupSan Mateo · United States2026 · 15 paperscurrent🎓Inner Mongolia UniversityHohhot · China2026 · 2 paperscurrent🎓Northeastern UniversityShenyang · China2020–2022 · 11 papers🎓Peking UniversityBeijing · China2020–2021 · 3 papers🎓Renmin University of ChinaBeijing · China2024–2025 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 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.

📅 Works by year

2020s

📄 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

5 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. A Novel Global Feature-Oriented Relational Triple Extraction Model based on Table Filling ↗Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · no summary yetFeiliang Ren et al.2021 · 93 citations
2. 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
3. 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
4. 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
5. 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.

• Bo Zheng ×3
• Haibin Chen ×3
• Langming Liu ×3
• Wenbo Su ×3
• Yujin Yuan ×3
• Kangtao Lv ×2
• Weidong Zhang ×2
• Xin Tong ×2
• Hai-Tao Zheng
• Jiaang Li
• Jiwei Tang
• Qingsong Lv

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) ×3: ACL 2026 ×2
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
• Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

🔗 Cited by

Papers of our library cite their works 19 times (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated 1 November 2026

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