---
title: "Who cites Yiran Chen — 311 papers of our library"
description: "311 papers of the Elamur Research library cite the works of Yiran Chen, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/yiran-chen/cited-by/"
---
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# Who cites Yiran Chen

311 citations of their works from 311 papers of our library (of the 149,439 whose bibliographies we read) · 27 with a page here, the most cited first · [← Yiran Chen](https://md.research.elamur.ai/free/en/authors/yiran-chen/)

1. [**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/)
2. [**BARTScore: Evaluating Generated Text as Text Generation arXiv:2106.11520**Weizhe Yuan, Graham Neubig, Pengfei Liu*2021 · 815 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2021-bartscore-evaluating-generated-text-text/)
3. [**Reducing Transformer Depth on Demand with Structured Dropout arXiv:1909.11556**Angela Fan, Edouard Grave, Armand Joulin*2019 · 560 citations*](https://md.research.elamur.ai/free/en/papers/fan-2019-reducing-transformer-depth-demand-structured/)
4. **[QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization](https://md.research.elamur.ai/free/en/papers/zhong-2021-qmsum-new-benchmark-query-based/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Ming Zhong, Da Yin, Tao Yu, et al.*2021 · 404 citations*
5. **[A Survey on Model Compression for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2024-survey-model-compression-large-language/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Xunyu Zhu, Jian Li, Yong Liu, et al.*2024 · 381 citations*
6. **[BRIO: Bringing Order to Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/liu-2022-brio-bringing-order-abstractive-summarization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yixin Liu, Pengfei Liu, Dragomir Radev, et al.*2022 · 360 citations*
7. **[SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/liu-2021-simcls-simple-framework-contrastive-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yixin Liu, Pengfei Liu*2021 · 341 citations*
8. **[GSum: A General Framework for Guided Neural Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/dou-2021-gsum-general-framework-guided-neural/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Zi-Yi Dou, Pengfei Liu, Hiroaki Hayashi, et al.*2021 · 322 citations*
9. **[TLDR: Extreme Summarization of Scientific Documents](https://md.research.elamur.ai/free/en/papers/cachola-2020-tldr-extreme-summarization-scientific-documents/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Isabel Cachola, Kyle Lo, Arman Cohan, et al.*2020 · 262 citations*
10. **[Re-evaluating Evaluation in Text Summarization](https://md.research.elamur.ai/free/en/papers/bhandari-2020-re-evaluating-evaluation-text-summarization/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Manik Bhandari, Pranav Narayan Gour, Atabak Ashfaq, et al.*2020 · 259 citations*
11. **[Detecting Hallucinated Content in Conditional Neural Sequence Generation](https://md.research.elamur.ai/free/en/papers/zhou-2021-detecting-hallucinated-content-conditional-neural/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chunting Zhou, Graham Neubig, Jiatao Gu, et al.*2021 · 232 citations*
12. **[Measuring Attribution in Natural Language Generation Models](https://md.research.elamur.ai/free/en/papers/rashkin-2023-measuring-attribution-natural-language-generation/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Hannah Rashkin, Vitaly Nikolaev, Matthew Lamm, et al.*2023 · 201 citations*
13. [**Efficient Processing of Deep Neural Networks: A Tutorial and Survey Proceedings of the IEEE 2017**Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, et al.*2017 · 146 citations*](https://md.research.elamur.ai/free/en/papers/sze-2017-efficient-processing-deep-neural-networks/)
14. **[What Have We Achieved on Text Summarization?](https://md.research.elamur.ai/free/en/papers/huang-2020-what-have-we-achieved-text/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Dandan Huang, Leyang Cui, Sen Yang, et al.*2020 · 143 citations*
15. [**Measuring the Intrinsic Dimension of Objective Landscapes arXiv:1804.08838**Chunyuan Li, Heerad Farkhoor, Rosanne Liu, et al.*2018 · 143 citations*](https://md.research.elamur.ai/free/en/papers/li-2018-measuring-intrinsic-dimension-objective-landscapes/)
16. [**Compositional Exemplars for In-context Learning arXiv:2302.05698**Jiacheng Ye, Zhiyong Wu, Jiangtao Feng, et al.*2023 · 120 citations*](https://md.research.elamur.ai/free/en/papers/ye-2023-compositional-exemplars-context-learning/)
17. [**Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation arXiv:2212.07981**Yixin Liu, Alexander R. Fabbri, Pengfei Liu, et al.*2022 · 117 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-revisiting-gold-standard-grounding-summarization/)
18. [**Accelerating Sparse Deep Neural Networks arXiv 2021 (NVIDIA)**Asit Mishra, Jorge Albericio Latorre, Jeff Pool, et al.*2021 · 97 citations*](https://md.research.elamur.ai/free/en/papers/mishra-2021-accelerating-sparse-deep-neural-networks/)
19. [**Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation arXiv:2307.15337**Xuefei Ning, Zinan Lin, Zixuan Zhou, et al.*2023 · 72 citations*](https://md.research.elamur.ai/free/en/papers/ning-2023-skeleton-thought-prompting-llms-efficient/)
20. [**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/)
21. [**Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications arXiv:2402.05162**Boyi Wei, Kaixuan Huang, Yangsibo Huang, et al.*2024 · 66 citations*](https://md.research.elamur.ai/free/en/papers/wei-2024-assessing-brittleness-safety-alignment-pruning/)
22. **[Sociodemographic Bias in Language Models: A Survey and Forward Path](https://md.research.elamur.ai/free/en/papers/gupta-2023-sociodemographic-bias-language-models-survey/) [Workshop on Gender Bias in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/gebnlp/)**Vipul Gupta, Pranav Narayanan Venkit, Shomir Wilson, et al.*2023 · 40 citations*
23. **[Self-supervised Speech Representations Still Struggle with African American Vernacular English](https://md.research.elamur.ai/free/en/papers/chang-2024-self-supervised-speech-representations-still/) [Interspeech](https://md.research.elamur.ai/free/en/venues/interspeech-usb/)**Kalvin Chang, Yi-Hui Chou, Jiatong Shi, et al.*2024 · 10 citations*
24. **[Contrastive Learning with Narrative Twins for Modeling Story Salience](https://md.research.elamur.ai/free/en/papers/sterner-2026-contrastive-learning-narrative-twins-modeling/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Igor Sterner, Alex Lascarides, Frank Keller*2026 · 5 citations*
25. **[FedReFT: Federated Representation Fine-Tuning with All-But-Me Aggregation](https://md.research.elamur.ai/free/en/papers/siddika-2026-fedreft-federated-representation-fine-tuning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Fatema Siddika, Md Anwar Hossen, Juan Pablo Munoz, et al.*2026*
26. **[LLEOT: A Privacy-Enhancing Offsite Tuning Framework via Loss Landscape Elevation](https://md.research.elamur.ai/free/en/papers/zhong-2026-lleot-privacy-enhancing-offsite-tuning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jin Zhong, Jinglin Liang, Tongtong Yang, et al.*2026*
27. **[Safe-FedLLM: Delving into the Safety of Federated Large Language Models](https://md.research.elamur.ai/free/en/papers/tao-2026-safe-fedllm-delving-into-safety/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mingxiang Tao, Yu Tian, Wenxuan Tu, et al.*2026*

284 more citing papers of our library have no page here yet.

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