---
title: "Who cites Shiyu Chang — 378 papers of our library"
description: "378 papers of the Elamur Research library cite the works of Shiyu Chang, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/shiyu-chang/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/shiyu-chang/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Shiyu Chang](https://md.research.elamur.ai/free/en/authors/shiyu-chang/) / cited by

# Who cites Shiyu Chang

380 citations of their works from 378 papers of our library (of the 141,840 whose bibliographies we read) · 25 with a page here, the most cited first · [← Shiyu Chang](https://md.research.elamur.ai/free/en/authors/shiyu-chang/)

1. [**Making Pre-trained Language Models Better Few-shot Learners**Tianyu Gao, Adam Fisch, Danqi Chen*2021 · 1,963 citations*](https://md.research.elamur.ai/free/en/papers/gao-2021-making-pre-trained-language-models/)
2. [**Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference**Timo Schick, Hinrich Schütze*2021 · 1,803 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-exploiting-cloze-questions-few-shot/)
3. [**ERASER: A Benchmark to Evaluate Rationalized NLP Models**Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, et al.*2020 · 821 citations*](https://md.research.elamur.ai/free/en/papers/deyoung-2020-eraser-benchmark-evaluate-rationalized-nlp/)
4. [**Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness?**Alon Jacovi, Yoav Goldberg*2020 · 748 citations*](https://md.research.elamur.ai/free/en/papers/jacovi-2020-faithfully-interpretable-nlp-systems-how/)
5. [**A Survey on Automated Fact-Checking**Zhijiang Guo, Michael Schlichtkrull, Andreas Vlachos*2022 · 646 citations*](https://md.research.elamur.ai/free/en/papers/guo-2022-survey-automated-fact-checking/)
6. [**The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale**Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, et al.*2024 · 404 citations*](https://md.research.elamur.ai/free/en/papers/penedo-2024-fineweb-datasets-decanting-web-finest/)
7. [**A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios**Michael A. Hedderich, Lukas Lange, Heike Adel, et al.*2021 · 400 citations*](https://md.research.elamur.ai/free/en/papers/hedderich-2021-survey-recent-approaches-natural-language/)
8. [**RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning**Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, et al.*2022 · 328 citations*](https://md.research.elamur.ai/free/en/papers/deng-2022-rlprompt-optimizing-discrete-text-prompts/)
9. [**Induction Networks for Few-Shot Text Classification**Ruiying Geng, Binhua Li, Yongbin Li, et al.*2019 · 263 citations*](https://md.research.elamur.ai/free/en/papers/geng-2019-induction-networks-few-shot-text/)
10. [**Personalizing Dialogue Agents via Meta-Learning**Andrea Madotto, Zhaojiang Lin, Chien-Sheng Wu, et al.*2019 · 246 citations*](https://md.research.elamur.ai/free/en/papers/madotto-2019-personalizing-dialogue-agents-meta-learning/)
11. [**Learning to Faithfully Rationalize by Construction**Sarthak Jain, Sarah Wiegreffe, Yuval Pinter, et al.*2020 · 223 citations*](https://md.research.elamur.ai/free/en/papers/jain-2020-learning-faithfully-rationalize-construction/)
12. [**Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 174 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/)
13. [**Zero-Shot Cross-Lingual Transfer with Meta Learning**Farhad Nooralahzadeh, Giannis Bekoulis, Johannes Bjerva, et al.*2020 · 171 citations*](https://md.research.elamur.ai/free/en/papers/nooralahzadeh-2020-zero-shot-cross-lingual-transfer/)
14. [**Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases**Ilias Chalkidis, Manos Fergadiotis, Dimitrios Tsarapatsanis, et al.*2021 · 167 citations*](https://md.research.elamur.ai/free/en/papers/chalkidis-2021-paragraph-level-rationale-extraction-through/)
15. [**DataComp-LM: In search of the next generation of training sets for language models**Jeffrey Li, Alex Fang, Georgios Smyrnis, et al.*2024 · 156 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-datacomp-lm-search-next-generation/)
16. [**Explaining NLP Models via Minimal Contrastive Editing (MiCE)**Alexis Ross, Ana Marasović, Matthew Peters*2021 · 154 citations*](https://md.research.elamur.ai/free/en/papers/ross-2021-explaining-nlp-models-minimal-contrastive/)
17. [**Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models**Yang Sui, Yu-Neng Chuang, Guanchu Wang, et al.*2025 · 153 citations*](https://md.research.elamur.ai/free/en/papers/sui-2025-stop-overthinking-survey-efficient-reasoning/)
18. [**RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder**Shitao Xiao, Zheng Liu, Yingxia Shao, et al.*2022 · 145 citations*](https://md.research.elamur.ai/free/en/papers/xiao-2022-retromae-pre-training-retrieval-oriented/)
19. [**An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction**Bhargavi Paranjape, Mandar Joshi, John Thickstun, et al.*2020 · 137 citations*](https://md.research.elamur.ai/free/en/papers/paranjape-2020-information-bottleneck-approach-controlling-conciseness/)
20. [**Scaling Sentence Embeddings with Large Language Models**Ting Jiang, Shaohan Huang, Zhongzhi Luan, et al.*2024 · 126 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2024-scaling-sentence-embeddings-large-language/)
21. [**SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval**Liang Wang, Nan Yang, Xiaolong Huang, et al.*2023 · 106 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-simlm-pre-training-representation-bottleneck/)
22. [**AnglE-optimized Text Embeddings**Xianming Li, Jing Li*2023 · 102 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-angle-optimized-text-embeddings/)
23. [**FineWeb2: One Pipeline to Scale Them All — Adapting Pre-Training Data Processing to Every Language**Guilherme Penedo, Hynek Kydliček, Vinko Sabolčec, et al.*2025 · 97 citations*](https://md.research.elamur.ai/free/en/papers/penedo-2025-fineweb2-one-pipeline-scale-them/)
24. [**Faithfulness Tests for Natural Language Explanations**Pepa Atanasova, Oana-Maria Camburu, Christina Lioma, et al.*2023 · 92 citations*](https://md.research.elamur.ai/free/en/papers/atanasova-2023-faithfulness-tests-natural-language-explanations/)
25. [**Mitigating Language Bias in Multilingual Sentence Embeddings for Cross-Lingual Similarity Estimation**Kanade Nonomura, Keita Fukushima, Risa Kondo, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/nonomura-2026-mitigating-language-bias-multilingual-sentence/)

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

- [← back to](https://md.research.elamur.ai/free/en/authors/shiyu-chang/) — Shiyu Chang