---
title: "Who cites Dieuwke Hupkes — page 2"
description: "1,427 papers of the Elamur Research library cite the works of Dieuwke Hupkes, found in their bibliographies · page 2 of 2 · 101–148; the most cited first."
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---
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# Who cites Dieuwke Hupkes

1,465 citations of their works from 1,427 papers of our library (of the 140,884 whose bibliographies we read) · 148 with a page here, the most cited first · page 2 of 2 · 101–148 · [← Dieuwke Hupkes](https://md.research.elamur.ai/free/en/authors/dieuwke-hupkes/)

[← prev](https://md.research.elamur.ai/free/en/authors/dieuwke-hupkes/cited-by/) [1](https://md.research.elamur.ai/free/en/authors/dieuwke-hupkes/cited-by/) **2**

101. [**Orca 2: Teaching Small Language Models How to Reason**Arindam Mitra, Luciano Del Corro, Shweti Mahajan, et al.*2023 · 160 citations*](https://md.research.elamur.ai/free/en/papers/mitra-2023-orca-2-teaching-small-language/)
102. [**Reasoning with Language Model Prompting: A Survey**Shuofei Qiao, Yixin Ou, Ningyu Zhang, et al.*2022 · 160 citations*](https://md.research.elamur.ai/free/en/papers/qiao-2022-reasoning-language-model-prompting-survey/)
103. [**LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression**Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang, et al.*2024 · 159 citations*](https://md.research.elamur.ai/free/en/papers/pan-2024-llmlingua-2-data-distillation-efficient/)
104. [**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/)
105. [**ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs**Justin Chen, Swarnadeep Saha, Mohit Bansal*2024 · 154 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-reconcile-round-table-conference-improves/)
106. [**Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs**Microsoft, Abdelrahman Abouelenin, Atabak Ashfaq, et al.*2025 · 145 citations*](https://md.research.elamur.ai/free/en/papers/microsoft-2025-phi-4-mini-technical-report/)
107. [**Self-Evaluation Guided Beam Search for Reasoning**Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/xie-2023-self-evaluation-guided-beam-search/)
108. [**How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model**Michael Hanna, Ollie Liu, Alexandre Variengien*2023 · 140 citations*](https://md.research.elamur.ai/free/en/papers/hanna-2023-gpt-2-compute-greater-than/)
109. [**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/)
110. [**A Roadmap to Pluralistic Alignment**Taylor Sorensen, Jared Moore, Jillian Fisher, et al.*2024 · 135 citations*](https://md.research.elamur.ai/free/en/papers/sorensen-2024-roadmap-pluralistic-alignment/)
111. [**InFoBench: Evaluating Instruction Following Ability in Large Language Models**Yiwei Qin, Kaiqiang Song, Yebowen Hu, et al.*2024 · 133 citations*](https://md.research.elamur.ai/free/en/papers/qin-2024-infobench-evaluating-instruction-following-ability/)
112. [**To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning**Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez, et al.*2024 · 133 citations*](https://md.research.elamur.ai/free/en/papers/sprague-2024-cot-cot-chain-thought-helps/)
113. [**FLUTE: Figurative Language Understanding through Textual Explanations**Tuhin Chakrabarty, Arkadiy Saakyan, Debanjan Ghosh, et al.*2022 · 131 citations*](https://md.research.elamur.ai/free/en/papers/chakrabarty-2022-flute-figurative-language-understanding-through/)
114. [**Faithful Logical Reasoning via Symbolic Chain-of-Thought**Jundong Xu, Hao Fei, Liangming Pan, et al.*2024 · 129 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-faithful-logical-reasoning-symbolic-chain/)
115. [**Deductive Verification of Chain-of-Thought Reasoning**Zhan Ling, Yunhao Fang, Xuanlin Li, et al.*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/ling-2023-deductive-verification-chain-thought-reasoning/)
116. [**ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding**Uri Shaham, Maor Ivgi, Avia Efrat, et al.*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/shaham-2023-zeroscrolls-zero-shot-benchmark-long/)
117. [**Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions**Federico Bianchi, Mirac Suzgun, Giuseppe Attanasio, et al.*2023 · 124 citations*](https://md.research.elamur.ai/free/en/papers/bianchi-2023-safety-tuned-llamas-lessons-improving/)
118. [**Large Language Model Alignment: A Survey**Tianhao Shen, Renren Jin, Yufei Huang, et al.*2023 · 123 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-model-alignment-survey/)
119. [**DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence**DeepSeek-AI, Anyi Xu, Bangcai Lin, et al.*2026 · 117 citations*](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2026-deepseek-v4-highly-efficient/)
120. [**Evaluating the Performance of Large Language Models on GAOKAO Benchmark**Xiaotian Zhang, Chunyang Li, Yi Zong, et al.*2023 · 107 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-evaluating-performance-large-language/)
121. [**Inverse Scaling: When Bigger Isn't Better**Ian R. McKenzie, Alexander Lyzhov, Michael Pieler, et al.*2023 · 99 citations*](https://md.research.elamur.ai/free/en/papers/mckenzie-2023-inverse-scaling-bigger-isn-t/)
122. [**MAmmoTH2: Scaling Instructions from the Web**Xiang Yue, Tuney Zheng, Ge Zhang, et al.*2024 · 94 citations*](https://md.research.elamur.ai/free/en/papers/yue-2024-mammoth2-scaling-instructions-web/)
123. [**NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation**Kaustubh Dhole, Varun Gangal, Sebastian Gehrmann, et al.*2023 · 84 citations*](https://md.research.elamur.ai/free/en/papers/dhole-2023-nl-augmenter-framework-task-sensitive/)
124. [**PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities**Settaluri Sravanthi, Meet Doshi, Pavan Tankala, et al.*2024 · 75 citations*](https://md.research.elamur.ai/free/en/papers/sravanthi-2024-pub-pragmatics-understanding-benchmark-assessing/)
125. [**Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws**Nikhil Sardana, Jacob Portes, Sasha Doubov, et al.*2024 · 47 citations*](https://md.research.elamur.ai/free/en/papers/sardana-2024-chinchilla-optimal-accounting-inference-language/)
126. [**Metaphor Understanding Challenge Dataset for LLMs**Xiaoyu Tong, Rochelle Choenni, Martha Lewis, et al.*2024 · 47 citations*](https://md.research.elamur.ai/free/en/papers/tong-2024-metaphor-understanding-challenge-dataset-llms/)
127. [**Pragmatic competence of pre-trained language models through the lens of discourse connectives**Lalchand Pandia, Yan Cong, Allyson Ettinger*2021 · 42 citations*](https://md.research.elamur.ai/free/en/papers/pandia-2021-pragmatic-competence-pre-trained-language/)
128. [**Transcending Scaling Laws with 0.1% Extra Compute**Yi Tay, Jason Wei, Hyung Won Chung, et al.*2022 · 40 citations*](https://md.research.elamur.ai/free/en/papers/tay-2022-transcending-scaling-laws-0-1/)
129. [**Language acquisition: do children and language models follow similar learning stages?**Linnea Evanson, Yair Lakretz, Jean-Rémi King*2023 · 33 citations*](https://md.research.elamur.ai/free/en/papers/evanson-2023-language-acquisition-children-language-models/)
130. [**ACES: Translation Accuracy Challenge Sets for Evaluating Machine Translation Metrics**Chantal Amrhein, Nikita Moghe, Liane Guillou*2022 · 29 citations*](https://md.research.elamur.ai/free/en/papers/amrhein-2022-aces-translation-accuracy-challenge-sets/)
131. [**Machine Translation Meta Evaluation through Translation Accuracy Challenge Sets**Nikita Moghe, Arnisa Fazla, Chantal Amrhein, et al.*2025 · 28 citations*](https://md.research.elamur.ai/free/en/papers/moghe-2025-machine-translation-meta-evaluation-through/)
132. [**Observational Scaling Laws and the Predictability of Language Model Performance**Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto*2024 · 28 citations*](https://md.research.elamur.ai/free/en/papers/ruan-2024-observational-scaling-laws-predictability-language/)
133. [**Prediction of Arabic Legal Rulings using Large Language Models**Adel Ammar, Anis Koubaa, Bilel Benjdira, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/ammar-2023-prediction-arabic-legal-rulings-using/)
134. [**The Quantization Model of Neural Scaling**Eric J. Michaud, Ziming Liu, Uzay Girit, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/michaud-2023-quantization-model-neural-scaling/)
135. [**Semantics of Multiword Expressions in Transformer-Based Models: A Survey**Filip Miletić, Sabine Schulte im Walde*2024 · 20 citations*](https://md.research.elamur.ai/free/en/papers/miletic-2024-semantics-multiword-expressions-transformer-based/)
136. [**Do Large Language Models Understand Word Senses?**Domenico Meconi, Simone Stirpe, Federico Martelli*2025 · 18 citations*](https://md.research.elamur.ai/free/en/papers/meconi-2025-llms-word-senses/)
137. [**Construction Grammar Provides Unique Insight into Neural Language Models**Leonie Weissweiler, Taiqi He, Naoki Otani, et al.*2023 · 18 citations*](https://md.research.elamur.ai/free/en/papers/weissweiler-2023-construction-grammar-provides-unique-insight/)
138. [**Causal Interventions Reveal Shared Structure Across English Filler–Gap Constructions**Sasha Boguraev, Christopher Potts, Kyle Mahowald*2025 · 13 citations*](https://md.research.elamur.ai/free/en/papers/boguraev-2025-causal-interventions-reveal-shared-structure/)
139. [**Dialogue Discourse Parsing as Generation: A Sequence-to-Sequence LLM-based Approach**Chuyuan Li, Yuwei Yin, Giuseppe Carenini*2024 · 11 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-dialogue-discourse-parsing-as-generation/)
140. [**Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models**Francesca Padovani, Jaap Jumelet, Yevgen Matusevych, et al.*2025 · 9 citations*](https://md.research.elamur.ai/free/en/papers/padovani-2025-child-directed-language-does-not/)
141. [**Construction Grammar Evidence for How LLMs Use Context-Directed Extrapolation to Solve Tasks**Harish Tayyar Madabushi, Claire Bonial*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/madabushi-2025-construction-grammar-evidence-how-llms/)
142. [**EduBench: A Portuguese Benchmark for Open-Ended Discursive Question Answering**Pedro Henrique Paiola, Luís Gabriel Damiati Mendes, Bruno de Oliveira Monchelato, et al.*2026*](https://md.research.elamur.ai/free/en/papers/paiola-2026-edubench-portuguese-benchmark-open-ended/)
143. [**Open-access Dataset on Acceptability Ratings of Korean Clausal Constructions by Humans and GPT Models**Gyu-Ho Shin, Soo-Hwan Lee, Chanyoung Lee*2026*](https://md.research.elamur.ai/free/en/papers/shin-2026-open-access-dataset-acceptability-ratings/)
144. [**Persona-Aware Evaluation of Cognitive Bias in LLMs: From Benchmark to Applied Decision-Making**Katsumasa Yoshikawa, Junya Takayama, Takato Yamazaki*2026*](https://md.research.elamur.ai/free/en/papers/yoshikawa-2026-persona-aware-evaluation-cognitive-bias/)
145. [**Sentiment Analysis and Language Models for Kwanyama**Ndapa Nakashole*2026*](https://md.research.elamur.ai/free/en/papers/nakashole-2026-sentiment-analysis-language-models-kwanyama/)
146. [**Word predictability estimates from language models are not robust to tokenizer vocabulary**Kien Nguyen, Suhas Arehalli*2026*](https://md.research.elamur.ai/free/en/papers/nguyen-2026-word-predictability-estimates-language-models/)
147. [**ZIP: Quantifying Which Words Matter in Zero-Shot Instructional Prompts**Nikta Gohari Sadr, Sangmitra Madhusudan, Arash Asgari, et al.*2026*](https://md.research.elamur.ai/free/en/papers/sadr-2026-zip-quantifying-which-words-matter/)
148. [**Complexity factors of language models in reading comprehension: a controlled experiment (Étude des facteurs de complexité des modèles de langage dans une tâche de compréhension de lecture à l'aide d'une expérience contrôlée sémantiquement)**Elie Antoine, Frederic Bechet, Géraldine Damnati, et al.*2024*](https://md.research.elamur.ai/free/en/papers/antoine-2024-etude-des-facteurs-de-complexite/)

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