---
title: "Microsoft Research Asia — works"
description: "41 works by researchers of Microsoft Research Asia in our library, the most cited first."
canonical: "https://research.elamur.ai/free/en/companies/microsoft-research-asia/works/"
---
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# Works of Microsoft Research Asia

41 works · the most cited first · [← Microsoft Research Asia](https://md.research.elamur.ai/free/en/companies/microsoft-research-asia/)

1. [**Swin Transformer: Hierarchical Vision Transformer using Shifted Windows**Ze Liu, Yutong Lin, Yue Cao, et al.*2021 · 33,826 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-swin-transformer-hierarchical-vision-transformer/)
2. [**A Survey on Evaluation of Large Language Models**Yupeng Chang, Xu Wang, Jindong Wang, et al.*2023 · 3,640 citations*](https://md.research.elamur.ai/free/en/papers/chang-2023-survey-evaluation-large-language-models/)
3. [**CodeBERT: A Pre-Trained Model for Programming and Natural Languages**Zhangyin Feng, Daya Guo, Duyu Tang, et al.*2020 · 2,954 citations*](https://md.research.elamur.ai/free/en/papers/feng-2020-codebert-pre-trained-model-programming/)
4. [**Attention-based LSTM for Aspect-level Sentiment Classification**Yequan Wang, Minlie Huang, Xiaoyan Zhu, et al.*2016 · 2,609 citations*](https://md.research.elamur.ai/free/en/papers/wang-2016-attention-based-lstm-aspect-level/)
5. [**brat: a Web-based Tool for NLP-Assisted Text Annotation**Pontus Stenetorp, Sampo Pyysalo, Goran Topić, et al.*2012 · 1,368 citations*](https://md.research.elamur.ai/free/en/papers/stenetorp-2012-brat-web-based-tool-nlp/)
6. [**LayoutLM: Pre-training of Text and Layout for Document Image Understanding**Yiheng Xu, Minghao Li, Lei Cui, et al.*2019 · 889 citations*](https://md.research.elamur.ai/free/en/papers/xu-2019-layoutlm-pre-training-text-layout/)
7. [**Gated Self-Matching Networks for Reading Comprehension and Question Answering**Wenhui Wang, Nan Yang, Furu Wei, et al.*2017 · 823 citations*](https://md.research.elamur.ai/free/en/papers/wang-2017-gated-self-matching-networks-reading/)
8. [**LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking**Yupan Huang, Tengchao Lv, Lei Cui, et al.*2022 · 774 citations*](https://md.research.elamur.ai/free/en/papers/huang-2022-layoutlmv3-pre-training-document-ai/)
9. [**UniXcoder: Unified Cross-Modal Pre-training for Code Representation**Daya Guo, Shuai Lu, Nan Duan, et al.*2022 · 768 citations*](https://md.research.elamur.ai/free/en/papers/guo-2022-unixcoder-unified-cross-modal-pre/)
10. [**K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters**Ruize Wang, Duyu Tang, Nan Duan, et al.*2020 · 662 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-k-adapter-infusing-knowledge-pre/)
11. [**Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots**Yu Wu, Wei Wu, Chen Xing, et al.*2017 · 624 citations*](https://md.research.elamur.ai/free/en/papers/wu-2017-sequential-matching-network-new-architecture/)
12. [**LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding**Yang Xu, Yiheng Xu, Tengchao Lv, et al.*2021 · 623 citations*](https://md.research.elamur.ai/free/en/papers/xu-2021-layoutlmv2-multi-modal-pre-training/)
13. [**ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training**Weizhen Qi, Yu Yan, Yeyun Gong, et al.*2020 · 451 citations*](https://md.research.elamur.ai/free/en/papers/qi-2020-prophetnet-predicting-future-n-gram/)
14. [**HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization**Xingxing Zhang, Furu Wei, Ming Zhou*2019 · 446 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2019-hibert-document-level-pre-training/)
15. [**Neural Document Summarization by Jointly Learning to Score and Select Sentences**Qingyu Zhou, Nan Yang, Furu Wei, et al.*2018 · 446 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2018-neural-document-summarization-jointly-learning/)
16. [**Recognizing Named Entities in Tweets**Xiaohua Liu, Shaodian Zhang, Furu Wei, et al.*2011 · 435 citations*](https://md.research.elamur.ai/free/en/papers/liu-2011-recognizing-named-entities-tweets/)
17. [**Template-Based Named Entity Recognition Using BART**Leyang Cui, Yu Wu, Jian Liu, et al.*2021 · 420 citations*](https://md.research.elamur.ai/free/en/papers/cui-2021-template-based-named-entity-recognition/)
18. [**CMMLU: Measuring massive multitask language understanding in Chinese**Haonan Li, Yixuan Zhang, Fajri Koto, et al.*2023 · 384 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-cmmlu-measuring-massive-multitask-language/)
19. [**Query Rewriting in Retrieval-Augmented Large Language Models**Xinbei Ma, Yeyun Gong, Pengcheng He, et al.*2023 · 383 citations*](https://md.research.elamur.ai/free/en/papers/ma-2023-query-rewriting-retrieval-augmented-large/)
20. [**Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy**Zhihong Shao, Yeyun Gong, Yelong Shen, et al.*2023 · 381 citations*](https://md.research.elamur.ai/free/en/papers/shao-2023-enhancing-retrieval-augmented-large-language/)
21. [**Question Generation for Question Answering**Nan Duan, Duyu Tang, Peng Chen, et al.*2017 · 370 citations*](https://md.research.elamur.ai/free/en/papers/duan-2017-question-generation-question-answering/)
22. [**GraphCodeBERT: Pre-training Code Representations with Data Flow**Daya Guo, Shuo Ren, Shuai Lu, et al.*2021 · 328 citations*](https://md.research.elamur.ai/free/en/papers/guo-2021-graphcodebert-pre-training-code-representations/)
23. [**CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing**Zhibin Gou, Zhihong Shao, Yeyun Gong, et al.*2023 · 315 citations*](https://md.research.elamur.ai/free/en/papers/gou-2023-critic-large-language-models-self/)
24. [**Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models**Chenfei Wu, Shengming Yin, Weizhen Qi, et al.*2023 · 315 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-visual-chatgpt-talking-drawing-editing/)
25. [**PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts**Kaijie Zhu, Jindong Wang, Jiaheng Zhou, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-promptrobust-towards-evaluating-robustness-large/)
26. [**Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data**Canwen Xu, Daya Guo, Nan Duan, et al.*2023 · 294 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-baize-open-source-chat-model/)
27. [**Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks**Haoyang Huang, Yaobo Liang, Nan Duan, et al.*2019 · 261 citations*](https://md.research.elamur.ai/free/en/papers/huang-2019-unicoder-universal-language-encoder-pre/)
28. [**TAPEX: Table Pre-training via Learning a Neural SQL Executor**Qian Liu, Bei Chen, Jiaqi Guo, et al.*2021 · 254 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-tapex-table-pre-training-learning/)
29. [**Fine-Tuning LLaMA for Multi-Stage Text Retrieval**Xueguang Ma, Liang Wang, Nan Yang, et al.*2023 · 254 citations*](https://md.research.elamur.ai/free/en/papers/ma-2023-fine-tuning-llama-multi-stage/)
30. [**TrustLLM: Trustworthiness in Large Language Models**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 226 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)
31. [**Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting**Haoyang Huang, Tianyi Tang, Dongdong Zhang, et al.*2023 · 223 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-all-languages-created-equal-llms/)
32. [**MuTual: A Dataset for Multi-Turn Dialogue Reasoning**Leyang Cui, Yu Wu, Shujie Liu, et al.*2020 · 189 citations*](https://md.research.elamur.ai/free/en/papers/cui-2020-mutual-dataset-multi-turn-dialogue/)
33. [**PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization**Yidong Wang, Zhuohao Yu, Zhengran Zeng, et al.*2023 · 189 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-pandalm-automatic-evaluation-benchmark-llm/)
34. [**Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models**Tianyi Tang, Wenyang Luo, Haoyang Huang, et al.*2024 · 151 citations*](https://md.research.elamur.ai/free/en/papers/tang-2024-language-specific-neurons-key-multilingual/)
35. [**AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators**Xingwei He, Zhenghao Lin, Yeyun Gong, et al.*2023 · 150 citations*](https://md.research.elamur.ai/free/en/papers/he-2023-annollm-making-large-language-models/)
36. [**rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking**Xinyu Guan, Li Lyna Zhang, Yifei Liu, et al.*2025 · 146 citations*](https://md.research.elamur.ai/free/en/papers/guan-2025-rstar-math-small-llms-can/)
37. [**Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding**Heming Xia, Zhe Yang, Qingxiu Dong, et al.*2024 · 145 citations*](https://md.research.elamur.ai/free/en/papers/xia-2024-unlocking-efficiency-large-language-model/)
38. [**Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration**Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, et al.*2023 · 139 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-unleashing-emergent-cognitive-synergy-large/)
39. [**BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation**Yuchen Jiang, Tianyu Liu, Shuming Ma, et al.*2022 · 85 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2022-blonde-automatic-evaluation-metric-document/)
40. [**A Multi-Task Dataset for Assessing Discourse Coherence in Chinese Essays: Structure, Theme, and Logic Analysis**Hongyi Wu, Xinshu Shen, Man Lan, et al.*2023 · 12 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-multi-task-dataset-assessing-discourse/)
41. [**THU_NGN at SemEval-2018 Task 1: Fine-grained Tweet Sentiment Intensity Analysis with Attention CNN-LSTM**Chuhan Wu, Fangzhao Wu, Junxin Liu, et al.*2018 · 10 citations*](https://md.research.elamur.ai/free/en/papers/wu-2018-thu-ngn-semeval-2018-task/)
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