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Yu Liu

Yu Liu has 2 papers in our library, in 2026, most of them on Language models & pretraining, Information retrieval & search; often with Jie Sun, Jun Zhou, Pengfei Liu.

2 papers in our library · 2026

OpenAlex · ORCID · Wikidata · h-index 2 · 10 works · 16 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1 time in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #11749

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

🏛️Chinese Academy of SciencesBeijing · China2024–2026 · 3 paperscurrent🔬Institute of Information EngineeringBeijing · China2024–2026 · 3 paperscurrent🎓South China University of TechnologyGuangzhou · China2026 · 3 paperscurrent🎓University of Chinese Academy of SciencesBeijing · China2024–2026 · 3 paperscurrent🎓University of Electronic Science and Technology of ChinaChengdu · China2026 · 2 paperscurrent🎓Fudan UniversityShanghai · China2026 · 1 papercurrent🎓Nanjing UniversityNanjing · 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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 workSignal Processing · 1 work

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

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs (2026 · 1 citation)
    Modern LLMs advertise very long context windows, yet they handle long sequences of numbers poorly. Tasks such as counting, filtering, or spotting trends need exact access to every value. The authors trace this failure to attention dispersion: as inputs grow, Softmax spreads attention over many similar numeric tokens…
  2. PRA-RAG: Provably Robust Aggregation in Retrieval-Augmented Generation against Retrieval Corruption (2026)
    Retrieval-Augmented Generation can be hijacked when attackers plant crafted passages in the knowledge base so that the LLM returns an answer they chose.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMsFindings of the Association for Computational LinguisticsJie Sun, Yu Liu, Lu Han, et al.2026 · 1 citation
  2. PRA-RAG: Provably Robust Aggregation in Retrieval-Augmented Generation against Retrieval CorruptionFindings of the Association for Computational LinguisticsXue Tan, Yi Zheng, Chang Huo, et al.2026
  3. Two Streams, One Sarcasm: Orthogonal Expert Tuning for Holistic Multimodal Sarcasm Understanding ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetDiandian Guo et al.2026

 Co-authors here

8 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 1 time (of the 149,439 papers whose bibliographies we read).

Updated

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