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Linbo Qiao

Linbo Qiao has 2 papers in our library, in 2019–2026, most of them on Language models & pretraining, Information extraction, Efficient models & compression; often with Dongsheng Li, Sen Yang.

2 papers in our library · 2019–2026

OpenAlex · ORCID · Wikidata · h-index 18 · 111 works · 1,694 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 106 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #4042

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

🎓National University of Defense TechnologyChangsha · China2012–2026 · 101 paperscurrent

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).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Information extraction · 1 workEfficient models & compression · 1 workLanguage models & pretraining · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Information extraction · 1 work

 Works by year

2020s

20261 paper

2010s

20191 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Exploring Pre-trained Language Models for Event Extraction and Generation (2019 · 454 citations)
    The paper addresses two weaknesses of ACE-style event extraction. First, one argument often fills several roles in the same event; about 10% of ACE2005 events show this "roles overlap" problem. Second, hand-labeled training data is scarce. The authors propose PLMEE, a two-stage extractor built on BERT. A token-level…
  2. GRASS: Gradient-based Adaptive Layer-wise Importance Sampling for Memory-efficient Large Language Model Fine-tuning (2026)
    Full-parameter fine-tuning of large language models needs a lot of GPU memory. LoRA-style methods reduce this cost but lose expressiveness. Layer-wise methods such as LISA reduce memory by training only a few layers at a time. However, they choose those layers with fixed rules that ignore how layer relevance changes…

All works

3 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. Exploring Pre-trained Language Models for Event Extraction and GenerationAnnual Meeting of the Association for Computational Linguistics (ACL)Sen Yang, Dawei Feng, Linbo Qiao, et al.2019 · 454 citations
  2. GRASS: Gradient-based Adaptive Layer-wise Importance Sampling for Memory-efficient Large Language Model Fine-tuningFindings of the Association for Computational LinguisticsKaiyuan Tian, Linbo Qiao, Yu Tang, et al.2026
  3. Emotion Trajectory-aware Retrieval for Markov-driven Emotion Anticipation in LLM-based Emotional Support Conversation ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetHongyan Wu et al.2026

 Co-authors here

2 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 106 times (of the 149,439 papers whose bibliographies we read); 10 citing papers with a page here →.

Updated

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