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Wei Chen

Wei Chen has 7 papers in our library, in 2016–2026, most of them on Language models & pretraining, Information extraction, Sentiment & emotion; often with Chao Zhang, Enhong Chen, Fei Huang.

7 papers in our library · 2016–2026

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

📄 7 works in our library · 🔗 Cited 218 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #3182

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

🎓Guilin University of Electronic TechnologyGuilin · China2026 · 4 paperscurrent🎓Huazhong University of Science and TechnologyWuhan · China2026 · 2 paperscurrent🎓Inner Mongolia UniversityHohhot · China2026 · 2 paperscurrent🏢Microsoft Research AsiaBeijing · China2026 · 2 paperscurrent🎓University of Science and Technology of ChinaHefei · China2026 · 2 paperscurrent🎓Zhejiang UniversityHangzhou · China2026 · 2 paperscurrent🎓Kunming University of Science and TechnologyKunming · China2026 · 1 papercurrent

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 12The field of science this work belongs to (OpenAlex fields).Computer Science · 7 worksArtificial Intelligence · 6 worksArts and Humanities · 5 worksLanguage and Linguistics · 5 worksHealth Informatics · 2 worksMedicine · 2 worksHealth Information Management · 1 workHealth Professions · 1 workPsychology · 1 workComputer Vision and Pattern Recognition · 2 worksExperimental and Cognitive Psychology · 1 workInformation Systems · 1 work

🧭 Topics 5Research subjects and languages the work is about.Language models & pretraining · 2 worksSentiment & emotion · 1 workInformation extraction · 1 workBias, fairness & safety · 1 workEfficient models & compression · 1 work

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

 Works by year

2020s

20262 papers20252 papers20241 paper20231 paper

2010s

20161 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Large Language Models for Generative Information Extraction: A Survey (2023 · 406 citations)
    Information extraction (IE) turns raw text into structured knowledge such as entities, relations and events.
  2. pkudblab at SemEval-2016 Task 6 : A Specific Convolutional Neural Network System for Effective Stance Detection (2016 · 155 citations)
    The paper describes pkudblab's system for SemEval-2016 Task 6, which classifies tweets as favoring, opposing, or neutral toward a given target.
  3. Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning (2024 · 63 citations)
    When an instruction-tuned LLM is further fine-tuned for a narrow downstream task, it tends to lose general abilities such as instruction following, helpfulness and safety alignment.
  4. AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator (2025 · 36 citations)
    Medical QA benchmarks give a model the whole case at once. Real diagnosis is different: a clinician must gather facts step by step through conversation and tests. To measure this skill, the authors built AI Hospital, a multi-agent simulator. An LLM under test plays the Doctor, while GPT-3.5 drives two non-player…
  5. LLMs Can Simulate Standardized Patients via Agent Coevolution (2025 · 20 citations)
    Medical training relies on standardized patients (SPs), actors who portray real cases. They are costly to prepare, and the role can strain their well-being. LLM-based SPs exist, but earlier work mostly improved retrieval or relied on expert feedback. The harder problem is teaching a knowledgeable model to answer like…
  6. Demonstrating ViviDoc: Generating Interactive Documents through Human-Agent Collaboration (2026 · 1 citation)
    Interactive explanatory articles, with sliders and visuals that readers can manipulate, help people learn, but they are expensive to build because they need both subject expertise and web programming.
  7. SAMoRA: Semantic-Aware Mixture of LoRA Experts for Task-Adaptive Learning (2026)
    Combining Mixture-of-Experts with LoRA (MoE-LoRA) is a common way to fine-tune LLMs on many tasks at once.

All works

11 works of theirs in our library, the most cited first; 7 with a page here, the others link their original until we summarise them.

  1. Large Language Models for Generative Information Extraction: A SurveyFrontiers of Computer ScienceDerong Xu, Wei Chen, Wenjun Peng, et al.2023 · 406 citations
  2. pkudblab at SemEval-2016 Task 6 : A Specific Convolutional Neural Network System for Effective Stance DetectionInternational Workshop on Semantic Evaluation (SemEval)Wan Wei, Xiao Zhang, Xuqin Liu, et al.2016 · 155 citations
  3. Self-Distillation Bridges Distribution Gap in Language Model Fine-TuningAnnual Meeting of the Association for Computational Linguistics (ACL)Zhaorui Yang, Tianyu Pang, Haozhe Feng, et al.2024 · 63 citations
  4. AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction SimulatorInternational Conference on Computational Linguistics (COLING)Zhihao Fan, Lai Wei, Jialong Tang, et al.2025 · 36 citations
  5. LLMs Can Simulate Standardized Patients via Agent CoevolutionAnnual Meeting of the Association for Computational Linguistics (ACL)Zhuoyun Du, Lujie Zheng, Renjun Hu, et al.2025 · 20 citations
  6. Demonstrating ViviDoc: Generating Interactive Documents through Human-Agent CollaborationAnnual Meeting of the Association for Computational Linguistics (ACL)Yinghao Tang, Yupeng Xie, Yingchaojie Feng, et al.2026 · 1 citation
  7. SAMoRA: Semantic-Aware Mixture of LoRA Experts for Task-Adaptive LearningFindings of the Association for Computational LinguisticsBoyan Shi, Wei Chen, Shuyuan Zhao, et al.2026
  8. PaT: Planning-after-Trial for Efficient Test-Time Code Generation ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetYoungsik Yoon et al.2026
  9. Doc-V*: Coarse-to-Fine Interactive Visual Reasoning for Multi-Page Document VQA ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetYuanlei Zheng et al.2026
  10. Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetQimin Zhong et al.2026
  11. Attention as Selector: Unlocking VLM Attention for Long Document Page Retrieval ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetMinfeng Zhu et al.2026

 Co-authors here

12 of 20 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 218 times (of the 149,439 papers whose bibliographies we read); 46 citing papers with a page here →.

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