--- title: "Yin Zhang · Zhejiang University — author profile and papers" description: "Yin Zhang, Zhejiang University: 9 papers on Elamur Research (2021–2026), h-index 0 — affiliations, topics, co-authors and who cites them in…" canonical: "https://research.elamur.ai/free/en/authors/yin-zhang/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/yin-zhang/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Yin Zhang # Yin Zhang Yin Zhang has 9 papers in our library, in 2021–2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/), [Efficient models & compression](https://md.research.elamur.ai/free/en/topics/efficient-models/); often with [Chenglin Li](https://md.research.elamur.ai/free/en/authors/chenglin-li/), [Qianglong Chen](https://md.research.elamur.ai/free/en/authors/qianglong-chen/), [Hengwei Liu](https://md.research.elamur.ai/free/en/authors/hengwei-liu/). 9 papers in our library · 2021–2026 [OpenAlex](https://openalex.org/A5139843545) · [**h-index 0**](https://openalex.org/authors/A5139843545) · [4 works](https://openalex.org/works?filter=author.id:A5139843545) · [0 citations](https://openalex.org/works?filter=author.id:A5139843545&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 9 works in our library](https://md.research.elamur.ai/free/en/authors/yin-zhang/#papers) · [🔗 Cited 82 times in our library](https://md.research.elamur.ai/free/en/authors/yin-zhang/cited-by/) · [⭐ Paper Stars ranking: #7798](https://md.research.elamur.ai/free/en/paper-stars/page/78/#star-7798) 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=Yin+Zhang) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Yin+Zhang) · [dblp](https://dblp.org/search?q=Yin+Zhang) · [ACL Anthology](https://aclanthology.org/search/?q=Yin+Zhang) ## Affiliations [🎓**Zhejiang University** Hangzhou · China 2026 · 4 papers*current*](https://md.research.elamur.ai/free/en/universities/zhejiang-university/) 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 6 The field of science this work belongs to (OpenAlex fields).[Computer Science · 9 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Arts and Humanities · 8 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Artificial Intelligence · 9 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 8 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Information Systems · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) 🧭 Topics 3 Research subjects and languages the work is about.[Reasoning · 4 works](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Language models & pretraining · 5 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Efficient models & compression · 1 work](https://md.research.elamur.ai/free/en/topics/efficient-models/) ## Works by year ### 2020s [**2026** 5 papers](https://md.research.elamur.ai/free/en/authors/yin-zhang/year/2026/) · [**2024** 1 paper](https://md.research.elamur.ai/free/en/authors/yin-zhang/year/2024/) · [**2023** 1 paper](https://md.research.elamur.ai/free/en/authors/yin-zhang/year/2023/) · [**2022** 1 paper](https://md.research.elamur.ai/free/en/authors/yin-zhang/year/2022/) · [**2021** 1 paper](https://md.research.elamur.ai/free/en/authors/yin-zhang/year/2021/) ## Papers The most cited first (OpenAlex and our library). 1. **[ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding](https://md.research.elamur.ai/free/en/papers/peng-2022-ernie-layout-layout-knowledge/)** *(2022 · 40 citations)* Most document pre-training models treat layout as a 2D extension of 1D positions and serialize OCR output in raster-scan order (top-to-bottom, left-to-right). 2. **[KACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference](https://md.research.elamur.ai/free/en/papers/chen-2021-kace-generating-knowledge-aware-contrastive/)** *(2021 · 28 citations)* Most explanation generators in NLP only justify the chosen label ("why A"). This paper argues that contrastive explanations ("why A and not B") are more useful, both for users and for the model. KACE is a post-hoc framework for natural language inference built from three stages: 3. **[Teaching Small Language Models Reasoning through Counterfactual Distillation](https://md.research.elamur.ai/free/en/papers/feng-2024-teaching-small-language-models-reasoning/)** *(2024 · 21 citations)* The paper addresses a weakness of chain-of-thought (CoT) distillation, where a large teacher model's rationales are used to fine-tune small language models (SLMs). 4. **[Mixed Distillation Helps Smaller Language Model Better Reasoning](https://md.research.elamur.ai/free/en/papers/li-2023-mixed-distillation-helps-smaller-language/)** *(2023 · 11 citations)* Large language models reason well but are expensive to run. Earlier work on distilling reasoning into compact models mostly transfers only natural-language Chain of Thought (CoT). This paper argues that Program of Thought (PoT), where the model writes executable Python, is a valuable extra training signal. The… 5. **[VideoPro: Adaptive Program Reasoning for Long Video Understanding](https://md.research.elamur.ai/free/en/papers/li-2026-videopro-adaptive-program-reasoning-long/)** *(2026 · 6 citations)* Long videos are hard for VideoLLMs because the visual cues needed to answer a question are few and scattered across a long timeline. 6. **[AutoTaskEval: Towards Domain-Specific and Fine-Grained Evaluation for LLMs](https://md.research.elamur.ai/free/en/papers/lyu-2026-autotaskeval-domain-specific-fine-grained/)** *(2026)* Static, manually built benchmarks are expensive, cover few tasks, and lose discriminative power as LLMs improve. 7. **[Learning on Imbalanced Noisy Data via Debiased Sample Selection and LLM-Driven Annotation](https://md.research.elamur.ai/free/en/papers/yuan-2026-learning-imbalanced-noisy-data-debiased/)** *(2026)* Text classification data gathered through web crawling or crowdsourcing often has wrong labels, and real collections are usually class-imbalanced too. 8. **[Leveraging Outline-Optimized Generative Interactions and Critique for Self-Refining Outlines with Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/liu-2026-leveraging-outline-optimized-generative-interactions/)** *(2026)* Small language models struggle to produce hierarchical outlines for Wikipedia-style expository articles. Their outlines tend to skip levels, omit important branches and distribute depth poorly. Existing remedies such as STORM, OmniThink or RAPID rely on costly multi-turn LLM interaction or fixed refinement… 9. **[Vector Calligrapher: Generating Scalable Vector Graphics via Structured Linguistic Supervision](https://md.research.elamur.ai/free/en/papers/zhou-2026-vector-calligrapher-generating-scalable-vector/)** *(2026)* Multimodal LLMs struggle to turn a textual style request into an SVG font glyph for two reasons. Free-form captions describe typographic style too loosely to control exact geometry. Generic tokenizers also split coordinate-heavy SVG markup into long token streams that carry little information each. Vector… ## Co-authors here 12 of 34 co-authors with a page, the most papers together first. - [Chenglin Li](https://md.research.elamur.ai/free/en/authors/chenglin-li/) ×3 - [Qianglong Chen](https://md.research.elamur.ai/free/en/authors/qianglong-chen/) ×3 - [Hengwei Liu](https://md.research.elamur.ai/free/en/authors/hengwei-liu/) ×2 - [Qingqing Lyu](https://md.research.elamur.ai/free/en/authors/qingqing-lyu/) ×2 - [Weiming Lu](https://md.research.elamur.ai/free/en/authors/weiming-lu/) ×2 - [Yicheng Li](https://md.research.elamur.ai/free/en/authors/yicheng-li/) ×2 - [Yongliang Shen](https://md.research.elamur.ai/free/en/authors/yongliang-shen/) ×2 - [Bo Yuan](https://md.research.elamur.ai/free/en/authors/bo-yuan/) - [Bo Zhou](https://md.research.elamur.ai/free/en/authors/bo-zhou/) - [Daoxin Zhang](https://md.research.elamur.ai/free/en/authors/daoxin-zhang/) - [Fei Yu](https://md.research.elamur.ai/free/en/authors/fei-yu/) - [Feng Ji](https://md.research.elamur.ai/free/en/authors/feng-ji/) ## 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/) ×5: [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/) ×4, [ACL 2021](https://md.research.elamur.ai/free/en/venues/acl/2021/) - [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/): [EMNLP 2024](https://md.research.elamur.ai/free/en/venues/emnlp/2024/) - [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/) ## Cited by Papers of our library cite their works 82 times (of the 149,439 papers whose bibliographies we read); [21 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/yin-zhang/cited-by/). Updated 2 November 2026