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Binxing Jiao

Binxing Jiao has 2 papers in our library, in 2022–2023, most of them on Language models & pretraining, Information retrieval & search; often with Daxin Jiang, Furu Wei, Liang Wang.

2 papers in our library · 2022–2023

OpenAlex · ORCID · h-index 11 · 42 works · 472 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 630 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #2753

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

🏢StepFun AIShanghai · China2026 · 2 paperscurrent🎓Xiamen UniversityXiamen · China2026 · 1 papercurrent🏢MicrosoftRedmond · United States2021–2024 · 7 papers🎓University of Science and Technology of ChinaHefei · China2003–2012 · 2 papersEducation 2003–2007 · Bachelor, Electronic EngineeringEducation 2007–2012 · PhD, Electronic Engineering
🏛️Microsoft CorpRedmond · United States2012Employment since 2012 · Bing

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 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 worksInformation Systems · 2 worksLanguage and Linguistics · 2 works

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

 Works by year

2020s

20231 paperSimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval20221 paperText Embeddings by Weakly-Supervised Contrastive Pre-training

 Papers

The most cited first (OpenAlex and our library).

  1. Text Embeddings by Weakly-Supervised Contrastive Pre-training (2022 · 594 citations)
    Single-vector text embeddings are central to retrieval, clustering and classification. However, earlier unsupervised approaches trained on synthetic pairs, such as ICT or random cropping, could not beat BM25 without labeled fine-tuning. The authors present E5, a family of general-purpose embedding models trained…
  2. SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval (2023 · 106 citations)
    Dense retrievers usually start from general-purpose language models. However, models that score better on GLUE do not necessarily retrieve better. On MS-MARCO, BERT outperforms RoBERTa and ELECTRA. The authors attribute this to the [CLS] vector not being trained to capture a whole passage. SimLM is a self-supervised…

All works

6 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. Text Embeddings by Weakly-Supervised Contrastive Pre-trainingarXiv:2212.03533Liang Wang, Nan Yang, Xiaolong Huang, et al.2022 · 594 citations
  2. SimLM: Pre-training with Representation Bottleneck for Dense Passage RetrievalAnnual Meeting of the Association for Computational Linguistics (ACL)Liang Wang, Nan Yang, Xiaolong Huang, et al.2023 · 106 citations
  3. Step-Audio 2 Technical ReportarXiv:2507.16632 · original ↗ · no summary yetBoyong Wu et al.2025 · 31 citations
  4. Step-Audio: Unified Understanding and Generation in Intelligent Speech InteractionarXiv:2502.11946 · original ↗ · no summary yetAilin Huang et al.2025 · 23 citations
  5. Step1X-Edit: A Practical Framework for General Image EditingarXiv:2504.17761 · original ↗ · no summary yetShiyu Liu et al.2025 · 21 citations
  6. Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation ModelarXiv:2502.10248 · original ↗ · no summary yetGuoqing Ma et al.2025 · 20 citations

 Co-authors here

7 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 630 times (of the 141,840 papers whose bibliographies we read); 44 citing papers with a page here →.

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