--- title: "Linbo Qiao — author profile and papers — Elamur Research" description: "Linbo Qiao, National University of Defense Technology: 2 papers on Elamur Research (2019–2026), h-index 18 — affiliations, topics…" canonical: "https://research.elamur.ai/free/en/authors/linbo-qiao/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/linbo-qiao/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Linbo Qiao # Linbo Qiao Linbo Qiao has 2 papers in our library, in 2019–2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Information extraction](https://md.research.elamur.ai/free/en/topics/information-extraction/), [Efficient models & compression](https://md.research.elamur.ai/free/en/topics/efficient-models/); often with [Dongsheng Li](https://md.research.elamur.ai/free/en/authors/dongsheng-li/), [Sen Yang](https://md.research.elamur.ai/free/en/authors/sen-yang/). 2 papers in our library · 2019–2026 [OpenAlex](https://openalex.org/A5043045076) · [ORCID](https://orcid.org/0000-0002-8285-2738) · [Wikidata](https://www.wikidata.org/wiki/Q130948452) · [**h-index 18**](https://openalex.org/authors/A5043045076) · [111 works](https://openalex.org/works?filter=author.id:A5043045076) · [1,694 citations](https://openalex.org/works?filter=author.id:A5043045076&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/linbo-qiao/#papers) · [🔗 Cited 106 times in our library](https://md.research.elamur.ai/free/en/authors/linbo-qiao/cited-by/) · [1 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #4042](https://md.research.elamur.ai/free/en/paper-stars/page/41/#star-4042) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Linbo+Qiao) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Linbo+Qiao) · [dblp](https://dblp.org/search?q=Linbo+Qiao) · [ACL Anthology](https://aclanthology.org/search/?q=Linbo+Qiao) ## Affiliations [🎓**National University of Defense Technology** Changsha · China 2012–2026 · 101 papers*current*](https://md.research.elamur.ai/free/en/universities/national-university-of-defense-technology/) 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 5 The field of science this work belongs to (OpenAlex fields).[Artificial Intelligence · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Arts and Humanities · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Language and Linguistics · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) 🧭 Topics 3 Research subjects and languages the work is about.[Information extraction · 1 work](https://md.research.elamur.ai/free/en/topics/information-extraction/) · [Efficient models & compression · 1 work](https://md.research.elamur.ai/free/en/topics/efficient-models/) · [Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) 💡 Concepts 1 Key ideas and methods explained on our concept pages.[Information extraction · 1 work](https://md.research.elamur.ai/free/en/concepts/information-extraction/) ## Works by year ### 2020s [**2026** 1 paper](https://md.research.elamur.ai/free/en/authors/linbo-qiao/year/2026/) ### 2010s [**2019** 1 paper](https://md.research.elamur.ai/free/en/authors/linbo-qiao/year/2019/) ## Papers The most cited first (OpenAlex and our library). 1. **[Exploring Pre-trained Language Models for Event Extraction and Generation](https://md.research.elamur.ai/free/en/papers/yang-2019-exploring-pre-trained-language-models/)** *(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](https://md.research.elamur.ai/free/en/papers/tian-2026-grass-gradient-based-adaptive-layer/)** *(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 Generation](https://md.research.elamur.ai/free/en/papers/yang-2019-exploring-pre-trained-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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-tuning](https://md.research.elamur.ai/free/en/papers/tian-2026-grass-gradient-based-adaptive-layer/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kaiyuan Tian, Linbo Qiao, Yu Tang, et al.*2026* 3. **[Emotion Trajectory-aware Retrieval for Markov-driven Emotion Anticipation in LLM-based Emotional Support Conversation ↗](https://aclanthology.org/2026.findings-acl.2127/) Findings of the Association for Computational Linguistics: ACL 2026 · no summary yet**Hongyan Wu et al.*2026* ## Co-authors here 2 co-authors with a page, the most papers together first. - [Dongsheng Li](https://md.research.elamur.ai/free/en/authors/dongsheng-li/) ×2 - [Sen Yang](https://md.research.elamur.ai/free/en/authors/sen-yang/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/): [ACL 2019](https://md.research.elamur.ai/free/en/venues/acl/2019/) - [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/): [Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/) - Findings of the Association for Computational Linguistics: ACL 2026 ## 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 →](https://md.research.elamur.ai/free/en/authors/linbo-qiao/cited-by/). Updated 1 November 2026