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Jiachen Liu

Jiachen Liu has 2 papers in our library, in 2021–2023, most of them on Language models & pretraining, Vision & language; often with Can Gao, Che Liu, Haifeng Wang.

2 papers in our library · 2021–2023

OpenAlex · ORCID · h-index 16 · 62 works · 1,506 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 231 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #4726

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

🎓Beihang UniversityBeijing · China2018–2026 · 4 paperscurrent🎓University of MichiganAnn Arbor · United States2023–2026 · 3 paperscurrent🏢BaiduBeijing · China2018–2023 · 16 papers🎓Xidian UniversityXi'an · China2012–2016 · 12 papers🔬Dana-Farber Cancer InstituteBoston · United States2022–2025 · 2 papers🎓Peking UniversityBeijing · China2022–2023 · 2 papers🎓Wuhan UniversityWuhan · China2023 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 13 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 5The 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 works

🧭 Topics 2Research subjects and languages the work is about.Vision & language · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20231 paper20211 paper

 Papers

The most cited first (OpenAlex and our library).

  1. UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning (2021 · 308 citations)
    Existing pre-trained models usually serve only one side. Text-only and vision-only models such as BERT or ResNet cannot handle vision-language inputs. Multi-modal models such as ViLBERT and UNITER learn only from scarce image-text pairs and perform poorly when moved to pure language tasks. The authors (Baidu) propose…
  2. Efficient Large Language Models: A Survey (2023 · 110 citations)
    Training and running LLMs with billions of parameters is costly. Using LLaMA data, the authors show that training compute rises steeply with model size while inference throughput falls. This survey maps research on cutting these costs and limits itself to techniques for billion-scale models rather than smaller…

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. UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Wei Li, Can Gao, Guocheng Niu, et al.2021 · 308 citations
  2. Leveraging Graph to Improve Abstractive Multi-Document Summarization ↗Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · no summary yetWei Li et al.2020 · 168 citations
  3. Efficient Large Language Models: A SurveyarXiv:2312.03863Zhongwei Wan, Xin Wang, Che Liu, et al.2023 · 110 citations
  4. Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer ↗Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · no summary yetChulun Zhou et al.2020 · 52 citations

 Co-authors here

12 of 14 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 231 times (of the 149,439 papers whose bibliographies we read); 29 citing papers with a page here →.

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