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Yangyifan Xu

Yangyifan Xu has 2 papers in our library, in 2025, most of them on Language models & pretraining; often with Jiajun Zhang, Jianghao Chen, Junhong Wu.

2 papers in our library · 2025

OpenAlex · h-index 3 · 12 works · 29 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 20 times in our library · ⭐ Paper Stars ranking: #8896

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 · China2024–2025 · 7 paperscurrent🔬Institute of AutomationBeijing · China2024–2025 · 7 paperscurrent🎓University of Chinese Academy of SciencesBeijing · China2024–2025 · 7 paperscurrent🎓Beijing Jiaotong UniversityBeijing · China2021 · 3 papers

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 5The 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 work

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

 Works by year

2020s

20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. LADM: Long-context Training Data Selection with Attention-based Dependency Measurement for LLMs (2025 · 12 citations)
    Extending LLMs to long inputs usually relies on continual pre-training with long documents. However, there has been no reliable way to judge whether a long sample actually contains meaningful long-range connections or is just loosely joined text. The authors first show that this matters. Llama2-7B trained on 32K…
  2. Hit the Sweet Spot! Span-Level Ensemble for Large Language Models (2025 · 11 citations)
    Combining several large language models can exploit their complementary strengths, but current ensembling works at two awkward granularities.

 Co-authors here

3 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 20 times (of the 149,439 papers whose bibliographies we read); 6 citing papers with a page here →.

Updated

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