---
title: "Yichun Yin — author profile and papers — Elamur Research"
description: "Yichun Yin, North China Electric Power University: 6 papers on Elamur Research (2020–2026), h-index 15 — affiliations, topics, co-authors…"
canonical: "https://research.elamur.ai/free/en/authors/yichun-yin/"
---
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# Yichun Yin

Yichun Yin has 6 papers in our library, in 2020–2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Information retrieval & search](https://md.research.elamur.ai/free/en/topics/information-retrieval/), [Efficient models & compression](https://md.research.elamur.ai/free/en/topics/efficient-models/); often with [Lifeng Shang](https://md.research.elamur.ai/free/en/authors/lifeng-shang/), [Qun Liu](https://md.research.elamur.ai/free/en/authors/qun-liu/), [Wei Guo](https://md.research.elamur.ai/free/en/authors/wei-guo/).

6 papers in our library · 2020–2026

[OpenAlex](https://openalex.org/A5102209620) · [ORCID](https://orcid.org/0000-0001-5567-8861) · [**h-index 15**](https://openalex.org/authors/A5102209620) · [36 works](https://openalex.org/works?filter=author.id:A5102209620) · [1,484 citations](https://openalex.org/works?filter=author.id:A5102209620&sort=cited_by_count:desc) OpenAlex, 2026-10-14

[📄 6 works in our library](https://md.research.elamur.ai/free/en/authors/yichun-yin/#papers) · [🔗 Cited 913 times in our library](https://md.research.elamur.ai/free/en/authors/yichun-yin/cited-by/) · [5 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #1199](https://md.research.elamur.ai/free/en/paper-stars/page/12/#star-1199)

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=Yichun+Yin) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Yichun+Yin) · [dblp](https://dblp.org/search?q=Yichun+Yin) · [ACL Anthology](https://aclanthology.org/search/?q=Yichun+Yin)

## Affiliations

[🎓**North China Electric Power University** Beijing · China 2024–2026 · 8 papers*current*](https://md.research.elamur.ai/free/en/universities/north-china-electric-power-university/) · [🔬**State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources** Beijing 2025–2026 · 7 papers*current*](https://ror.org/045hfth47) · [🎓**Monash University** Melbourne · Australia 2016–2020 · 17 papers](https://md.research.elamur.ai/free/en/universities/monash-university/) · [🏢**Huawei Technologies** Shenzhen · China 2021–2023 · 4 papers](https://md.research.elamur.ai/free/en/companies/huawei-technologies/) · [🔬**ARC Centre of Excellence for Electromaterials Science** Wollongong · Australia 2016–2018 · 3 papers](https://ror.org/020g3wn65) · [🎓**Dongguan University of Technology** Dongguan · China 2018–2019 · 2 papers](https://ror.org/01m8p7q42)

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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 7 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 6 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 6 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 5 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/) · [Hardware and Architecture · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/hardware-and-architecture/)

🧭 Topics 4 Research subjects and languages the work is about.[Efficient models & compression · 2 works](https://md.research.elamur.ai/free/en/topics/efficient-models/) · [Language models & pretraining · 2 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Information retrieval & search · 2 works](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · [Reasoning · 1 work](https://md.research.elamur.ai/free/en/topics/reasoning/)

💡 Concepts 1 Key ideas and methods explained on our concept pages.[Natural language understanding · 1 work](https://md.research.elamur.ai/free/en/concepts/natural-language-understanding/)

## Works by year

### 2020s

[**2026** 1 paper](https://md.research.elamur.ai/free/en/authors/yichun-yin/year/2026/) · [**2025** 2 papers](https://md.research.elamur.ai/free/en/authors/yichun-yin/year/2025/) · [**2023** 1 paper](https://md.research.elamur.ai/free/en/authors/yichun-yin/year/2023/) · [**2020** 2 papers](https://md.research.elamur.ai/free/en/authors/yichun-yin/year/2020/)

## Papers

The most cited first (OpenAlex and our library).

1. **[TinyBERT: Distilling BERT for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/jiao-2020-tinybert-distilling-bert-natural-language/)** *(2020 · 2,378 citations)* Large pre-trained language models such as BERT achieve very high performance. However, they have many parameters and slow inference, which makes them difficult to run on phones and other low-resource devices. The authors compress BERT with a knowledge distillation method designed specifically for the Transformer…
2. **[TernaryBERT: Distillation-aware Ultra-low Bit BERT](https://md.research.elamur.ai/free/en/papers/zhang-2020-ternarybert-distillation-aware-ultra-low/)** *(2020 · 213 citations)* Fine-tuned BERT models are large: BERT-base has 109M parameters, over 400 MB in 32-bit floats. This makes them hard to run on phones and other constrained hardware. Earlier quantization work kept accuracy at 8 bits. Going lower usually needed mixed precision, which some hardware handles poorly, or product…
3. **[DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning](https://md.research.elamur.ai/free/en/papers/xiong-2023-dq-lore-dual-queries-low/)** *(2023 · 27 citations)* The order and choice of demonstrations in a prompt strongly affect how well large language models solve multi-step reasoning problems.
4. **[Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs](https://md.research.elamur.ai/free/en/papers/tang-2025-pangu-ultra-moe-train/)** *(2025 · 8 citations)* Training MoE language models with nearly a trillion parameters is hard: the hardware is underused because the set of experts each token activates changes at runtime, and the scale strains communication and memory.
5. **[Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs](https://md.research.elamur.ai/free/en/papers/yin-2025-pangu-ultra-pushing-limits/)** *(2025 · 6 citations)* Pangu Ultra is Huawei's effort to show that a very deep dense Transformer, trained entirely on Ascend NPUs instead of GPUs, can compete with much larger sparse MoE systems.
6. **[MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers](https://md.research.elamur.ai/free/en/papers/ma-2026-match-modulating-attention-context-retrieval/)** *(2026)* Sparse-attention LLMs, such as models built on sliding windows, save compute and memory. They tend to fail, however, when an answer depends on recalling an exact detail far back in the input. MATCH tackles this by pairing a sparsified Transformer with a lightweight retrieval component that treats the prompt itself as…

## All works

11 works of theirs in our library, the most cited first; 6 with a page here, the others link their original until we summarise them.

1. **[TinyBERT: Distilling BERT for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/jiao-2020-tinybert-distilling-bert-natural-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaoqi Jiao, Yichun Yin, Lifeng Shang, et al.*2020 · 2,378 citations*
2. **[TernaryBERT: Distillation-aware Ultra-low Bit BERT](https://md.research.elamur.ai/free/en/papers/zhang-2020-ternarybert-distillation-aware-ultra-low/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wei Zhang, Lu Hou, Yichun Yin, et al.*2020 · 213 citations*
3. **[Generate & Rank: A Multi-task Framework for Math Word Problems ↗](https://aclanthology.org/2021.findings-emnlp.195/) Findings of the Association for Computational Linguistics: EMNLP 2021 · no summary yet**Jianhao Shen et al.*2021 · 152 citations*
4. **[Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension ↗](https://aclanthology.org/D17-1217/) Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · no summary yet**Yichun Yin, Yangqiu Song, Ming Zhang*2017 · 99 citations*
5. **[FIMO: A Challenge Formal Dataset for Automated Theorem Proving ↗](https://arxiv.org/abs/2309.04295) arXiv:2309.04295 · no summary yet**Chengwu Liu et al.*2023 · 53 citations*
6. [**DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning arXiv:2310.02954**Jing Xiong, Zixuan Li, Chuanyang Zheng, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/xiong-2023-dq-lore-dual-queries-low/)
7. **[TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models ↗](https://aclanthology.org/2023.emnlp-main.711/) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · no summary yet**Jing Xiong et al.*2023 · 21 citations*
8. **[Safe: Enhancing Mathematical Reasoning in Large Language Models via Retrospective Step-aware Formal Verification ↗](https://arxiv.org/abs/2506.04592) arXiv:2506.04592 · no summary yet**Chengwu Liu et al.*2025 · 17 citations*
9. [**Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs arXiv:2505.04519**Yehui Tang, Yichun Yin, Yaoyuan Wang, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/tang-2025-pangu-ultra-moe-train/)
10. [**Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs arXiv:2504.07866**Yichun Yin, Wenyong Huang, Kaikai Song, et al.*2025 · 6 citations*](https://md.research.elamur.ai/free/en/papers/yin-2025-pangu-ultra-pushing-limits/)
11. **[MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers](https://md.research.elamur.ai/free/en/papers/ma-2026-match-modulating-attention-context-retrieval/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Linrui Ma, Chun Hei Lo, Xinyu Wang, et al.*2026*

## Co-authors here

12 of 29 co-authors with a page, the most papers together first.

- [Lifeng Shang](https://md.research.elamur.ai/free/en/authors/lifeng-shang/) ×3
- [Qun Liu](https://md.research.elamur.ai/free/en/authors/qun-liu/) ×2
- [Wei Guo](https://md.research.elamur.ai/free/en/authors/wei-guo/) ×2
- [Xiao Chen](https://md.research.elamur.ai/free/en/authors/xiao-chen/) ×2
- [Xin Jiang](https://md.research.elamur.ai/free/en/authors/xin-jiang/) ×2
- [Yaoyuan Wang](https://md.research.elamur.ai/free/en/authors/yaoyuan-wang/) ×2
- [Yehui Tang](https://md.research.elamur.ai/free/en/authors/yehui-tang/) ×2
- [Can Chen](https://md.research.elamur.ai/free/en/authors/can-chen/)
- [Chengming Li](https://md.research.elamur.ai/free/en/authors/chengming-li/)
- [Dong Li](https://md.research.elamur.ai/free/en/authors/dong-li/)
- [Fangcheng Liu](https://md.research.elamur.ai/free/en/authors/fangcheng-liu/)
- [Hang Zhou](https://md.research.elamur.ai/free/en/authors/hang-zhou/)

## Venues

Where their works here were published, the most works first, with the editions of those that have a page.

- [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/) ×3: [EMNLP 2020](https://md.research.elamur.ai/free/en/venues/emnlp/2020/)
- [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/): [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)
- [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/): [Findings of the Association for Computational Linguistics 2020](https://md.research.elamur.ai/free/en/venues/findings/2020/)
- Findings of the Association for Computational Linguistics: EMNLP 2021

## Cited by

Papers of our library cite their works 913 times (of the 149,439 papers whose bibliographies we read); [108 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/yichun-yin/cited-by/).

Updated 1 November 2026