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Yingxia Shao

Yingxia Shao has 2 papers in our library, in 2022–2024, most of them on Language models & pretraining, Information retrieval & search; often with Shitao Xiao, Zheng Liu, Defu Lian.

2 papers in our library · 2022–2024

OpenAlex · ORCID · Wikidata · h-index 30 · 167 works · 2,696 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 158 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #6419

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

🎓Beijing University of Posts and TelecommunicationsBeijing · China2019–2026 · 138 papersEmploymentcurrent🏢InspurJinan · China2026 · 1 papercurrent🎓Peking UniversityBeijing · China2013–2022 · 19 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 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).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 work

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

 Works by year

2020s

20241 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder (2022 · 145 citations)
    Standard language models such as BERT learn mainly through token-level objectives. These objectives do little to build strong whole-sentence embeddings, which limits the models as encoders for dense retrieval. Contrastive pre-training could help, but it depends on good data augmentation and on many negatives.…
  2. Making Text Embedders Few-Shot Learners (2024 · 60 citations)
    LLM-based text embedders perform well on tasks they were trained on, but they often fail to follow unfamiliar instructions or handle complex retrieval, because training covers only a narrow range of instructions.

All works

4 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. RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-EncoderConference on Empirical Methods in Natural Language Processing (EMNLP)Shitao Xiao, Zheng Liu, Yingxia Shao, et al.2022 · 145 citations
  2. Distributed Graph Neural Network Training: A Survey ↗arXiv:2211.00216 · no summary yetYingxia Shao et al.2022 · 103 citations
  3. Causal Intervention and Counterfactual Reasoning for Multi-modal Fake News Detection ↗Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetZiwei Chen et al.2023 · 72 citations
  4. Making Text Embedders Few-Shot LearnersarXiv:2409.15700Chaofan Li, MingHao Qin, Shitao Xiao, et al.2024 · 60 citations

 Co-authors here

6 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 158 times (of the 149,439 papers whose bibliographies we read); 25 citing papers with a page here →.

Updated

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