---
title: "Who cites Yichun Yin — 854 papers of our library"
description: "854 papers of the Elamur Research library cite the works of Yichun Yin, found in their bibliographies · page 1 of 2 · 1–100; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/yichun-yin/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/yichun-yin/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Yichun Yin](https://md.research.elamur.ai/free/en/authors/yichun-yin/) / cited by

# Who cites Yichun Yin

913 citations of their works from 854 papers of our library (of the 149,439 whose bibliographies we read) · 108 with a page here, the most cited first · page 1 of 2 · 1–100 · [← Yichun Yin](https://md.research.elamur.ai/free/en/authors/yichun-yin/)

**1** [2](https://md.research.elamur.ai/free/en/authors/yichun-yin/cited-by/page/2/) [next →](https://md.research.elamur.ai/free/en/authors/yichun-yin/cited-by/page/2/)

1. [**Language Models are Few-Shot Learners arXiv:2005.14165**Tom B. Brown, Benjamin Mann, Nick Ryder, et al.*2020 · 14,178 citations*](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
2. [**Chain-of-Thought Prompting Elicits Reasoning in Large Language Models arXiv:2201.11903**Jason Wei, Xuezhi Wang, Dale Schuurmans, et al.*2022 · 11,499 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/)
3. [**Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683**Colin Raffel, Noam Shazeer, Adam Roberts, et al.*2019 · 11,325 citations*](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/)
4. [**ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education**Enkelejda Kasneci, Kathrin Seßler, Stefan Küchemann, et al.*2023 · 7,440 citations*](https://md.research.elamur.ai/free/en/papers/kasneci-2023-chatgpt-good-opportunities-challenges-large/)
5. **[Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.*2021 · 5,423 citations*
6. [**Training Verifiers to Solve Math Word Problems arXiv:2110.14168**Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, et al.*2021 · 3,796 citations*](https://md.research.elamur.ai/free/en/papers/cobbe-2021-training-verifiers-solve-math-word/)
7. [**Self-Consistency Improves Chain of Thought Reasoning in Language Models arXiv:2203.11171**Xuezhi Wang, Jason Wei, Dale Schuurmans, et al.*2022 · 3,121 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-self-consistency-improves-chain-thought/)
8. **[ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://md.research.elamur.ai/free/en/papers/khattab-2020-colbert-efficient-effective-passage/) [International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)](https://md.research.elamur.ai/free/en/venues/international-acm-sigir-conference-on-research-and-development-in-information/)**Omar Khattab, Matei Zaharia*2020 · 1,789 citations*
9. [**Pre-trained Models for Natural Language Processing: A Survey arXiv:2003.08271 · SCIENCE CHINA Technological Sciences,2020, 63, 1872-1897**Xipeng Qiu, Tianxiang Sun, Yige Xu, et al.*2020 · 1,759 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2020-pre-trained-models-natural-language/)
10. **[Efficient Transformers: A Survey](https://md.research.elamur.ai/free/en/papers/tay-2020-efficient-transformers-survey/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Yi Tay, Mostafa Dehghani, Dara Bahri, et al.*2020 · 1,539 citations*
11. [**Let's Verify Step by Step arXiv:2305.20050**Hunter Lightman, Vineet Kosaraju, Yura Burda, et al.*2023 · 1,412 citations*](https://md.research.elamur.ai/free/en/papers/lightman-2023-let-s-verify-step-step/)
12. [**DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing arXiv:2111.09543**Pengcheng He, Jianfeng Gao, Weizhu Chen*2021 · 1,320 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-debertav3-improving-deberta-using-electra/)
13. **[It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://md.research.elamur.ai/free/en/papers/schick-2021-it-s-not-just-size/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Timo Schick, Hinrich Schütze*2021 · 1,132 citations*
14. **[MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://md.research.elamur.ai/free/en/papers/sun-2020-mobilebert-compact-task-agnostic-bert/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiqing Sun, Hongkun Yu, Xiaodan Song, et al.*2020 · 1,016 citations*
15. [**GLM-130B: An Open Bilingual Pre-trained Model arXiv:2210.02414**Aohan Zeng, Xiao Liu, Zhengxiao Du, et al.*2022 · 786 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2022-glm-130b-open-bilingual-pre/)
16. **[FNet: Mixing Tokens with Fourier Transforms](https://md.research.elamur.ai/free/en/papers/thorp-2022-fnet-mixing-tokens-fourier-transforms/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, et al.*2022 · 597 citations*
17. [**MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models arXiv:2309.12284**Longhui Yu, Weisen Jiang, Han Shi, et al.*2023 · 565 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-metamath-bootstrap-your-own-mathematical/)
18. **[DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference](https://md.research.elamur.ai/free/en/papers/xin-2020-deebert-dynamic-early-exiting-accelerating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ji Xin, Raphael Tang, Jaejun Lee, et al.*2020 · 487 citations*
19. **[LLM-Pruner: On the Structural Pruning of Large Language Models](https://md.research.elamur.ai/free/en/papers/ma-2023-llm-pruner-structural-pruning-large/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xinyin Ma, Gongfan Fang, Xinchao Wang*2023 · 452 citations*
20. [**Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents arXiv:2201.07207**Wenlong Huang, Pieter Abbeel, Deepak Pathak, et al.*2022 · 443 citations*](https://md.research.elamur.ai/free/en/papers/huang-2022-language-models-zero-shot-planners/)
21. **[Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling](https://md.research.elamur.ai/free/en/papers/hofstatter-2021-efficiently-teaching-effective-dense-retriever/) [International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)](https://md.research.elamur.ai/free/en/venues/international-acm-sigir-conference-on-research-and-development-in-information/)**Sebastian Hofstatter, Sheng-Chieh Lin, Jheng-Hong Yang, et al.*2021 · 425 citations*
22. **[Security and Privacy Challenges of Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/das-2024-security-privacy-challenges-large-language/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu*2024 · 424 citations*
23. **[FastBERT: a Self-distilling BERT with Adaptive Inference Time](https://md.research.elamur.ai/free/en/papers/liu-2020-fastbert-self-distilling-bert-adaptive/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weijie Liu, Peng Zhou, Zhiruo Wang, et al.*2020 · 383 citations*
24. [**WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct arXiv:2308.09583**Haipeng Luo, Qingfeng Sun, Can Xu, et al.*2023 · 372 citations*](https://md.research.elamur.ai/free/en/papers/luo-2023-wizardmath-empowering-mathematical-reasoning-large/)
25. **[Parameter-Efficient Transfer Learning with Diff Pruning](https://md.research.elamur.ai/free/en/papers/guo-2021-parameter-efficient-transfer-learning-diff/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Demi Guo, Alexander Rush, Yoon Kim*2021 · 367 citations*
26. **[SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer](https://md.research.elamur.ai/free/en/papers/vu-2022-spot-better-frozen-model-adaptation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tu Vu, Brian Lester, Noah Constant, et al.*2022 · 306 citations*
27. **[Large Language Models are Better Reasoners with Self-Verification](https://md.research.elamur.ai/free/en/papers/weng-2023-large-language-models-are-better/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yixuan Weng, Minjun Zhu, Fei Xia, et al.*2023 · 304 citations*
28. **[ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers](https://md.research.elamur.ai/free/en/papers/yao-2022-zeroquant-efficient-affordable-post-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, et al.*2022 · 304 citations*
29. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
30. [**On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes arXiv:2306.13649**Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, et al.*2023 · 299 citations*](https://md.research.elamur.ai/free/en/papers/agarwal-2023-policy-distillation-language-models-learning/)
31. **[LLM-QAT: Data-Free Quantization Aware Training for Large Language Models](https://md.research.elamur.ai/free/en/papers/liu-2024-llm-qat-data-free-quantization/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zechun Liu, Barlas Oguz, Changsheng Zhao, et al.*2024 · 276 citations*
32. **[ZeroGen: Efficient Zero-shot Learning via Dataset Generation](https://md.research.elamur.ai/free/en/papers/ye-2022-zerogen-efficient-zero-shot-learning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiacheng Ye, Jiahui Gao, Qintong Li, et al.*2022 · 245 citations*
33. **[BERT-of-Theseus: Compressing BERT by Progressive Module Replacing](https://md.research.elamur.ai/free/en/papers/xu-2020-bert-theseus-compressing-bert-progressive/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Canwen Xu, Wangchunshu Zhou, Tao Ge, et al.*2020 · 240 citations*
34. **[Structured Pruning Learns Compact and Accurate Models](https://md.research.elamur.ai/free/en/papers/xia-2022-structured-pruning-learns-compact-accurate/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengzhou Xia, Zexuan Zhong, Danqi Chen*2022 · 221 citations*
35. **[Making Language Models Better Reasoners with Step-Aware Verifier](https://md.research.elamur.ai/free/en/papers/li-2023-making-language-models-better-reasoners/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yifei Li, Zeqi Lin, Shizhuo Zhang, et al.*2023 · 217 citations*
36. **[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*
37. **[DyLoRA: Parameter-Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation](https://md.research.elamur.ai/free/en/papers/valipour-2023-dylora-parameter-efficient-tuning-pre/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Mojtaba Valipour, Mehdi Rezagholizadeh, Ivan Kobyzev, et al.*2023 · 203 citations*
38. **[Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias](https://md.research.elamur.ai/free/en/papers/yu-2023-large-language-model-attributed-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yue Yu, Yuchen Zhuang, Jieyu Zhang, et al.*2023 · 202 citations*
39. **[Structured Pruning of Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2020-structured-pruning-large-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ziheng Wang, Jeremy Wohlwend, Tao Lei*2020 · 202 citations*
40. **[Block Pruning For Faster Transformers](https://md.research.elamur.ai/free/en/papers/lagunas-2021-block-pruning-faster-transformers/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**François Lagunas, Ella Charlaix, Victor Sanh, et al.*2021 · 199 citations*
41. **[The Right Tool for the Job: Matching Model and Instance Complexities](https://md.research.elamur.ai/free/en/papers/schwartz-2020-right-tool-job-matching-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Roy Schwartz, Gabriel Stanovsky, Swabha Swayamdipta, et al.*2020 · 196 citations*
42. **[Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning](https://md.research.elamur.ai/free/en/papers/alabi-2022-adapting-pre-trained-language-models/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Jesujoba O. Alabi, David Ifeoluwa Adelani, Marius Mosbach, et al.*2022 · 195 citations*
43. **[Compressing Large-Scale Transformer-Based Models: A Case Study on BERT](https://md.research.elamur.ai/free/en/papers/ganesh-2021-compressing-large-scale-transformer-based/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Prakhar Ganesh, Yao Chen, Xin Lou, et al.*2021 · 190 citations*
44. [**SqueezeLLM: Dense-and-Sparse Quantization arXiv:2306.07629**Sehoon Kim, Coleman Hooper, Amir Gholami, et al.*2023 · 170 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-squeezellm-dense-sparse-quantization/)
45. **[BinaryBERT: Pushing the Limit of BERT Quantization](https://md.research.elamur.ai/free/en/papers/bai-2021-binarybert-pushing-limit-bert-quantization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoli Bai, Wei Zhang, Lu Hou, et al.*2021 · 166 citations*
46. **[When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs](https://md.research.elamur.ai/free/en/papers/kamoi-2024-when-can-llms-actually-correct/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Ryo Kamoi, Yusen Zhang, Nan Zhang, et al.*2024 · 161 citations*
47. **[CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://md.research.elamur.ai/free/en/papers/zhang-2020-cpm-large-scale-generative/) [AI Open](https://md.research.elamur.ai/free/en/venues/ai-open/)**Zhengyan Zhang, Xu Han, Hao Zhou, et al.*2020 · 150 citations*
48. **[Efficient Methods for Natural Language Processing: A Survey](https://md.research.elamur.ai/free/en/papers/treviso-2023-efficient-methods-natural-language-processing/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Marcos Treviso, Ji-Ung Lee, Tianchu Ji, et al.*2023 · 145 citations*
49. [**MiniLLM: On-Policy Distillation of Large Language Models arXiv:2306.08543**Yuxian Gu, Li Dong, Furu Wei, et al.*2023 · 144 citations*](https://md.research.elamur.ai/free/en/papers/gu-2023-minillm-policy-distillation-large-language/)
50. **[Deductive Verification of Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/ling-2023-deductive-verification-chain-thought-reasoning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhan Ling, Yunhao Fang, Xuanlin Li, et al.*2023 · 130 citations*
51. [**LEVER: Learning to Verify Language-to-Code Generation with Execution arXiv:2302.08468**Ansong Ni, Srini Iyer, Dragomir Radev, et al.*2023 · 126 citations*](https://md.research.elamur.ai/free/en/papers/ni-2023-lever-learning-verify-language-code/)
52. **[I-BERT: Integer-only BERT Quantization](https://md.research.elamur.ai/free/en/papers/kim-2021-i-bert-integer-only-bert/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Sehoon Kim, Amir Gholami, Zhewei Yao, et al.*2021 · 122 citations*
53. **[MoEfication: Transformer Feed-forward Layers are Mixtures of Experts](https://md.research.elamur.ai/free/en/papers/zhang-2022-moefication-transformer-feed-forward-layers/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhengyan Zhang, Yankai Lin, Zhiyuan Liu, et al.*2022 · 115 citations*
54. [**DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data arXiv:2405.14333**Huajian Xin, Daya Guo, Zhihong Shao, et al.*2024 · 111 citations*](https://md.research.elamur.ai/free/en/papers/xin-2024-deepseek-prover-advancing-theorem-proving/)
55. [**Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction**Zhanming Jie, Jierui Li, Wei Lu*2022 · 102 citations*](https://md.research.elamur.ai/free/en/papers/jie-2022-learning-reason-deductively-math-word/)
56. **[f-Divergence Minimization for Sequence-Level Knowledge Distillation](https://md.research.elamur.ai/free/en/papers/wen-2023-f-divergence-minimization-sequence-level/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuqiao Wen, Zichao Li, Wenyu Du, et al.*2023 · 92 citations*
57. [**Universal Self-Consistency for Large Language Model Generation arXiv:2311.17311**Xinyun Chen, Renat Aksitov, Uri Alon, et al.*2023 · 92 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-universal-self-consistency-large-language/)
58. **[BERT Busters: Outlier Dimensions that Disrupt Transformers](https://md.research.elamur.ai/free/en/papers/kovaleva-2021-bert-busters-outlier-dimensions-that/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Olga Kovaleva, Saurabh Kulshreshtha, Anna Rogers, et al.*2021 · 90 citations*
59. **[LAMBADA: Backward Chaining for Automated Reasoning in Natural Language](https://md.research.elamur.ai/free/en/papers/kazemi-2023-lambada-backward-chaining-automated-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mehran Kazemi, Najoung Kim, Deepti Bhatia, et al.*2023 · 89 citations*
60. **[Retrieval-Augmented Generation for Natural Language Processing: A Survey](https://md.research.elamur.ai/free/en/papers/wu-2024-retrieval-augmented-generation-natural-language/) [Artificial Intelligence Review](https://md.research.elamur.ai/free/en/venues/artificial-intelligence-review/)**Shangyu Wu, Ying Xiong, Yufei Cui, et al.*2024 · 86 citations*
61. [**The Unreasonable Ineffectiveness of the Deeper Layers arXiv:2403.17887**Andrey Gromov, Kushal Tirumala, Hassan Shapourian, et al.*2024 · 71 citations*](https://md.research.elamur.ai/free/en/papers/gromov-2024-unreasonable-ineffectiveness-deeper-layers/)
62. **[A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models](https://md.research.elamur.ai/free/en/papers/stolfo-2023-causal-framework-quantify-robustness-mathematical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Alessandro Stolfo, Zhijing Jin, Kumar Shridhar, et al.*2023 · 65 citations*
63. [**ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation arXiv:2112.12731**Shuohuan Wang, Yu Sun, Yang Xiang, et al.*2021 · 63 citations*](https://md.research.elamur.ai/free/en/papers/wang-2021-ernie-3-0-titan/)
64. **[F-coref: Fast, Accurate and Easy to Use Coreference Resolution](https://md.research.elamur.ai/free/en/papers/otmazgin-2022-f-coref-fast-accurate-easy/) [Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL)](https://md.research.elamur.ai/free/en/venues/aacl/)**Shon Otmazgin, Arie Cattan, Yoav Goldberg*2022 · 45 citations*
65. **[Dual-Space Knowledge Distillation for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2024-dual-space-knowledge-distillation-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Songming Zhang, Xue Zhang, Zengkui Sun, et al.*2024 · 39 citations*
66. **[Improving Stance Detection with Multi-Dataset Learning and Knowledge Distillation](https://md.research.elamur.ai/free/en/papers/li-2021-improving-stance-detection-multi-dataset/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yingjie Li, Chenye Zhao, Cornelia Caragea*2021 · 39 citations*
67. [**Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs arXiv:2402.12030 · Transactions on Machine Learning Research, January 2025**Nicolas Boizard, Kevin El Haddad, Celine Hudelot, et al.*2024 · 35 citations*](https://md.research.elamur.ai/free/en/papers/boizard-2024-towards-cross-tokenizer-distillation-universal/)
68. [**A Survey of Small Language Models arXiv:2410.20011**Chien Van Nguyen, Xuan Shen, Ryan Aponte, et al.*2024 · 31 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2024-survey-small-language-models/)
69. **[AutoPSV: Automated Process-Supervised Verifier](https://md.research.elamur.ai/free/en/papers/lu-2024-autopsv-automated-process-supervised-verifier/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jianqiao Lu, Zhiyang Dou, Hongru Wang, et al.*2024 · 28 citations*
70. **[Towards the Law of Capacity Gap in Distilling Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2025-law-capacity-gap-distilling-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chen Zhang, Qiuchi Li, Dawei Song, et al.*2025 · 27 citations*
71. **[Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models](https://md.research.elamur.ai/free/en/papers/sun-2026-challenging-boundaries-reasoning-olympiad-level/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoxiang Sun, Yingqian Min, Zhipeng Chen, et al.*2026 · 25 citations*
72. **[FuseChat: Knowledge Fusion of Chat Models](https://md.research.elamur.ai/free/en/papers/wan-2025-fusechat-knowledge-fusion-chat-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Fanqi Wan, Longguang Zhong, Ziyi Yang, et al.*2025 · 25 citations*
73. [**Language Model Self-improvement by Reinforcement Learning Contemplation arXiv:2305.14483**Jing-Cheng Pang; Pengyuan Wang; Kaiyuan Li; Xiong-Hui Chen; Jiacheng Xu; Zongzhang Zhang;, Yang Yu*2023 · 22 citations*](https://md.research.elamur.ai/free/en/papers/pang-2023-language-model-self-improvement-reinforcement/)
74. **[AI4Research: A Survey of Artificial Intelligence for Scientific Research](https://md.research.elamur.ai/free/en/papers/chen-2025-ai4research-survey-artificial-intelligence/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Mingda Yang, Libo Qin, et al.*2025 · 21 citations*
75. **[M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/wu-2025-m-absa-multilingual-dataset-aspect/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**ChengYan Wu, Bolei Ma, Yihong Liu, et al.*2025 · 21 citations*
76. **[Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of In-Context Experts](https://md.research.elamur.ai/free/en/papers/le-2022-few-shot-anaphora-resolution-scientific/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nghia T. Le, Fan Bai, Alan Ritter*2022 · 20 citations*
77. [**FormalAlign: Automated Alignment Evaluation for Autoformalization arXiv:2410.10135**Jianqiao Lu, Yingjia Wan, Yinya Huang, et al.*2024 · 18 citations*](https://md.research.elamur.ai/free/en/papers/lu-2024-formalalign-automated-alignment-evaluation-autoformalization/)
78. **[Okay, Let's Do This! Modeling Event Coreference with Generated Rationales and Knowledge Distillation](https://md.research.elamur.ai/free/en/papers/nath-2024-okay-let-s-do-this/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Abhijnan Nath, Shadi Manafi Avari, Avyakta Chelle, et al.*2024 · 16 citations*
79. **[Pre-training Distillation for Large Language Models: A Design Space Exploration](https://md.research.elamur.ai/free/en/papers/peng-2025-pre-training-distillation-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hao Peng, Xin Lv, Yushi Bai, et al.*2025 · 14 citations*
80. [**Pangu Embedded: An Efficient Dual-system LLM Reasoner with Metacognition arXiv:2505.22375**Hanting Chen, Yasheng Wang, Kai Han, et al.*2025 · 13 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-pangu-embedded-efficient-dual/)
81. **[Distilling Calibrated Knowledge for Stance Detection](https://md.research.elamur.ai/free/en/papers/li-2023-distilling-calibrated-knowledge-stance-detection/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yingjie Li, Cornelia Caragea*2023 · 13 citations*
82. **[Looking at the Overlooked: An Analysis on the Word-Overlap Bias in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/rajaee-2022-looking-overlooked-analysis-word-overlap/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sara Rajaee, Yadollah Yaghoobzadeh, Mohammad Taher Pilehvar*2022 · 13 citations*
83. **[CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization](https://md.research.elamur.ai/free/en/papers/peng-2026-criticlean-critic-guided-reinforcement-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhongyuan Peng, Yifan Yao, Kaijing Ma, et al.*2026 · 12 citations*
84. **[EMO: Embedding Model Distillation via Intra-Model Relation and Optimal Transport Alignments](https://md.research.elamur.ai/free/en/papers/truong-2025-emo-embedding-model-distillation-intra/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Minh-Phuc Truong, Hai An Vu, Tu Vu, et al.*2025 · 10 citations*
85. **[Improving Reasoning Capabilities in Small Models through Mixture-of-layers Distillation with Stepwise Attention on Key Information](https://md.research.elamur.ai/free/en/papers/chen-2025-improving-reasoning-capabilities-small-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yao Chen, Jiawei Sheng, Wenyuan Zhang, et al.*2025 · 10 citations*
86. [**Knowledge Distillation of Black-Box Large Language Models arXiv:2401.07013**Hongzhan Chen, Ruijun Chen, Yuqi Yi, et al.*2024 · 10 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-knowledge-distillation-black-box-large/)
87. **[Expression Syntax Information Bottleneck for Math Word Problems](https://md.research.elamur.ai/free/en/papers/xiong-2023-expression-syntax-information-bottleneck-math/) [International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)](https://md.research.elamur.ai/free/en/venues/international-acm-sigir-conference-on-research-and-development-in-information/)**Jing Xiong, Chengming Li, Min Yang, et al.*2023 · 10 citations*
88. [**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/)
89. **[Cross-lingual transfer for low-resource Arabic language understanding](https://md.research.elamur.ai/free/en/papers/abboud-2022-cross-lingual-transfer-low-resource/) [Arabic Natural Language Processing Workshop (WANLP)](https://md.research.elamur.ai/free/en/venues/wanlp/)**Khadige Abboud, Olga Golovneva, Christopher DiPersio*2022 · 8 citations*
90. [**TinyR1-32B-Preview: Boosting Accuracy with Branch-Merge Distillation arXiv:2503.04872**Lin Sun, Guangxiang Zhao, Xiaoqi Jian, et al.*2025 · 7 citations*](https://md.research.elamur.ai/free/en/papers/sun-2025-tinyr1-32b-preview-boosting-accuracy/)
91. **[Sparse Logit Sampling: Accelerating Knowledge Distillation in LLMs](https://md.research.elamur.ai/free/en/papers/anshumann-2025-sparse-logit-sampling-accelerating-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Anshumann, Mohd Abbas Zaidi, Akhil Kedia, et al.*2025 · 6 citations*
92. [**Detecting Anti-Semitic Hate Speech using Transformer-based Large Language Models arXiv:2405.03794**Dengyi Liu, Minghao Wang, Andrew G. Catlin*2024 · 6 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-detecting-anti-semitic-hate-speech/)
93. **[Mathematical Proof as a Litmus Test: Revealing Failure Modes of Advanced Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/guo-2026-mathematical-proof-as-litmus-test/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dadi Guo, Jiayu Liu, Zhiyuan Fan, et al.*2026 · 5 citations*
94. **[Dynamic Sampling that Adapts: Self-Aware Iterative Data Persistent Optimization for Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/rao-2026-dynamic-sampling-that-adapts-self/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jun Rao, Xuebo Liu, Hexuan Deng, et al.*2026 · 4 citations*
95. [**Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity arXiv:2505.21411**Yehui Tang, Xiaosong Li, Fangcheng Liu, et al.*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/tang-2025-pangu-pro-moe-mixture/)
96. **[Balancing Fidelity and Plasticity: Aligning Mixed-Precision Fine-Tuning with Linguistic Hierarchies](https://md.research.elamur.ai/free/en/papers/zhou-2026-balancing-fidelity-plasticity-aligning-mixed/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Changhai Zhou, Shiyang Zhang, Yuhua Zhou, et al.*2026 · 3 citations*
97. **[Cross-layer Attention Sharing for Pre-trained Large Language Models](https://md.research.elamur.ai/free/en/papers/mu-2026-cross-layer-attention-sharing-pre/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Yongyu Mu, Yuzhang Wu, Yuchun Fan, et al.*2026 · 2 citations*
98. **[FLAT-LLM: Fine-grained Low-rank Activation Space Transformation for Large Language Model Compression](https://md.research.elamur.ai/free/en/papers/tian-2026-flat-llm-fine-grained-low/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiayi Tian, Ryan Solgi, Jinming Lu, et al.*2026 · 2 citations*
99. **[LEAF: Knowledge Distillation of Text Embedding Models with Teacher-Aligned Representations](https://md.research.elamur.ai/free/en/papers/vujanic-2026-leaf-knowledge-distillation-text-embedding/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Robin Vujanic, Thomas Rückstieß*2026 · 2 citations*
100. **[Multi-Granularity Semantic Revision for Large Language Model Distillation](https://md.research.elamur.ai/free/en/papers/liu-2026-multi-granularity-semantic-revision-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoyu Liu, Yun Zhang, Wei Li, et al.*2026 · 2 citations*

**1** [2](https://md.research.elamur.ai/free/en/authors/yichun-yin/cited-by/page/2/) [next →](https://md.research.elamur.ai/free/en/authors/yichun-yin/cited-by/page/2/)

- [← back to](https://md.research.elamur.ai/free/en/authors/yichun-yin/) — Yichun Yin