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Jianxin Liao

Jianxin Liao has 2 papers in our library, in 2026, most of them on Language models & pretraining, Low-resource languages; often with Haifeng Sun, Jingyu Wang, Cong Liu.

2 papers in our library · 2026

OpenAlex · ORCID · Wikidata · h-index 31 · 438 works · 4,607 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 64 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #11553

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 · China1998–2026 · 384 papersEducation since 1998current🔬State Key Laboratory of Networking and Switching TechnologyBeijing · China2005–2026 · 306 paperscurrent🎓University of Electronic Science and Technology of ChinaChengdu · China2022–2024 · 5 papers

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

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

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Erasing Without Remembering: Implicit Knowledge Forgetting in Large Language Models (2026 · 1 citation)
    The paper tackles a weakness in LLM machine unlearning: current techniques tend to suppress only the literal wording of the data to be forgotten.
  2. ModularMoE: Fast LLM Customization with Parameter-Sharing Mixture-of-Experts for Low-Resource Settings (2026)
    Running large language models on modest hardware is costly. Current customization routes either compress and fine-tune separately for every target task, which is expensive, or tune once on general data, which tends to hurt task-level quality through interference and forgetting. ModularMoE addresses this by turning an…

All works

5 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. Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis ↗Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · no summary yetChunning Du et al.2020 · 203 citations
  2. Capsule Network with Interactive Attention for Aspect-Level Sentiment Classification ↗Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · no summary yetChunning Du et al.2019 · 94 citations
  3. Question-Driven Span Labeling Model for Aspect–Opinion Pair Extraction ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetLei Gao et al.2021 · 80 citations
  4. Erasing Without Remembering: Implicit Knowledge Forgetting in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Huazheng Wang, Yongcheng Jing, Haifeng Sun, et al.2026 · 1 citation
  5. ModularMoE: Fast LLM Customization with Parameter-Sharing Mixture-of-Experts for Low-Resource SettingsFindings of the Association for Computational LinguisticsJiaxing Liu, Qi Qi, Haifeng Sun, et al.2026

 Co-authors here

9 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 64 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

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