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title: "Who cites Aidan Clark — page 3"
description: "4,354 papers of the Elamur Research library cite the works of Aidan Clark, found in their bibliographies · page 3 of 3 · 201–272; the most cited first."
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# Who cites Aidan Clark

4,729 citations of their works from 4,354 papers of our library (of the 140,884 whose bibliographies we read) · 272 with a page here, the most cited first · page 3 of 3 · 201–272 · [← Aidan Clark](https://md.research.elamur.ai/free/en/authors/aidan-clark/)

[← prev](https://md.research.elamur.ai/free/en/authors/aidan-clark/cited-by/page/2/) [1](https://md.research.elamur.ai/free/en/authors/aidan-clark/cited-by/) [2](https://md.research.elamur.ai/free/en/authors/aidan-clark/cited-by/page/2/) **3**

201. [**Arabizi vs LLMs: Can the Genie Understand the Language of Aladdin?**Perla Al Almaoui, Pierrette Bouillon, Simon Hengchen*2025 · 2 citations*](https://md.research.elamur.ai/free/en/papers/almaoui-2025-arabizi-vs-llms-can-genie/)
202. [**Children's English Reading Story Generation via Supervised Fine-Tuning of Compact LLMs with Controllable Difficulty and Safety**Qian Shen, Fanghua Cao, Min Yao, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/shen-2026-children-s-english-reading-story/)
203. [**CLASE: A Hybrid Method for Chinese Legalese Stylistic Evaluation**Yiran Rex Ma, Yuxiao Ye, Huiyuan Xie*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/ma-2026-clase-hybrid-method-chinese-legalese/)
204. [**Constructing a Japanese Claim Decomposition Dataset for Fact-Checking of LLM-Generated Texts**Miwa Masano, Ribeka Keyaki, Atsushi Keyaki, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/masano-2026-constructing-japanese-claim-decomposition-dataset/)
205. [**Do LLMs Judge Distantly Supervised Named Entity Labels Well? Constructing the JudgeWEL Dataset**Alistair Plum, Laura Maria Bernardy, Tharindu Ranasinghe*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/plum-2026-do-llms-judge-distantly-supervised/)
206. [**GIL-Zaragoza at SemEval 2026 Task 11: Comparing Classification, Autoformalization, and Ontologies for Formal Reasoning Capabilities**Francisco Lopez-Ponce, Lucia Pitarch, Iván Saavedra Martínez, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/ponce-2026-gil-zaragoza-semeval-2026-task/)
207. [**Is This Idea Novel? An Automated Benchmark for Judgment of Research Ideas**Tim Schopf, Michael Färber*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/schopf-2026-is-this-idea-novel-automated/)
208. [**Modeling Clinical Uncertainty in Radiology Reports: From Explicit Uncertainty Markers to Implicit Reasoning Pathways**Paloma Rabaey, Jong Hak Moon, Jung-Oh Lee, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/rabaey-2026-modeling-clinical-uncertainty-radiology-reports/)
209. [**Morphemes Without Borders: Evaluating Root-Pattern Morphology in Arabic Tokenizers and LLMs**Yara Alakeel, Chatrine Qwaider, Hanan Aldarmaki, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/alakeel-2026-morphemes-borders-evaluating-root-pattern/)
210. [**Morphemes without Borders: Evaluating Root–Pattern Morphology in Arabic Tokenizers and LLMs**Yara Yousif Alakeel, Chatrine Qwaider, Hanan Aldarmaki, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/alakeel-2026-morphemes-without-borders-evaluating-root/)
211. [**Multi-modal, Multi-task, Multi-criteria Automatic Evaluation with Vision Language Models**Masanari Oi, Masahiro Kaneko, Naoaki Okazaki, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/oi-2026-multi-modal-multi-task-multi/)
212. [**Revisiting Metric Reliability for Fine-grained Evaluation of Machine Translation and Summarization in Indian Languages**Amir Hossein Yari, Kalmit Kulkarni, Ahmad Raza Khan, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/yari-2026-revisiting-metric-reliability-fine-grained/)
213. [**Rhetorical Questions in LLM Representations: A Linear Probing Study**Louie Hong Yao, Vishesh Anand, Yuan Zhuang, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/yao-2026-rhetorical-questions-llm-representations-linear/)
214. [**SEFL: A Framework for Generating Synthetic Educational Assignment Feedback with LLM Agents**Mike Zhang, Amalie Pernille Dilling, Léon Gondelman, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/zhang-2026-sefl-framework-generating-synthetic-educational/)
215. [**Sense and Sensitivity: “Reasoning” Models are More Robust, but can Diverge from Human Consensus in a Legal Interpretation Task**Dawson Petersen, Abhishek Purushothama, Nathan Schneider*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/petersen-2026-sense-sensitivity-reasoning-models-are/)
216. [**Synthetic Function Demonstrations Improve Generation in Low-Resource Programming Languages**Nick McKenna, Xinnuo Xu, Jack Williams, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/mckenna-2026-synthetic-function-demonstrations-improve-generation/)
217. [**LCHAIM - Investigating Long Context Reasoning in Hebrew**Ehud Malul, Oriel Perets, Ziv Mor, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/malul-2025-lchaim-investigating-long-context-reasoning/)
218. [**A Dataset for Oral Reading in Young English Readers**Madison Rose, Michael Bennie, Valeria Pagliai, et al.*2026*](https://md.research.elamur.ai/free/en/papers/rose-2026-dataset-oral-reading-young-english/)
219. [**A Dialogue System for Second Language Learning with Dynamic Adaptation to In-Dialogue Difficulty Changes**Taku Morioka, Junya Takayama, Tomoyuki Kajiwara*2026*](https://md.research.elamur.ai/free/en/papers/morioka-2026-dialogue-system-second-language-learning/)
220. [**A Scalable Pipeline for Novelty Detection in Skill Extraction Using Large Language Models**Gian Seifert, Simon Clematide*2026*](https://md.research.elamur.ai/free/en/papers/seifert-2026-scalable-pipeline-novelty-detection-skill/)
221. [**A Single Model Ensemble Framework for Neural Machine Translation Using Pivot Translation**Seokjin Oh, Keonwoong Noh, Woohwan Jung*2026*](https://md.research.elamur.ai/free/en/papers/oh-2026-single-model-ensemble-framework-neural/)
222. [**A Survey on Parameter-efficient Fine-tuning of Large Models: Techniques, Trends, and Challenges (大模型参数高效微调方法综述: 技术、趋势与挑战)**Anda Tang (唐岸达), Zhouchen Lin (林宙辰)*2026*](https://md.research.elamur.ai/free/en/papers/tang-2026-parameter-efficient-fine/)
223. [**AraDynFact: Dynamic Evaluation of Factual Knowledge in Arabic**Ignacio Iacobacci, Faroq Altam, Zhaozhi Qian, et al.*2026*](https://md.research.elamur.ai/free/en/papers/iacobacci-2026-aradynfact-dynamic-evaluation-factual-knowledge/)
224. [**Assessing Y-Axis Influence: Bias in Multimodal Language Models on Chart-to-Table Translation**Seok Hwan Song, Azher Ahmed Efat, Wallapak Tavanapong*2026*](https://md.research.elamur.ai/free/en/papers/song-2026-assessing-y-axis-influence-bias/)
225. [**Augmenting LLM Reasoning with Dynamic Notes Writing for Complex MultiHop QA**Rishabh Maheshwary, Masoud Hashemi, Khyati Mahajan, et al.*2026*](https://md.research.elamur.ai/free/en/papers/maheshwary-2026-augmenting-llm-reasoning-dynamic-notes/)
226. [**Beyond Simple Term Injection: Reasoning Models for Legal Translation in a Non-Dominant Language Variety**Paolo Di Natale, Elena Chiocchetti, Marlies Alber, et al.*2026*](https://md.research.elamur.ai/free/en/papers/natale-2026-beyond-simple-term-injection-reasoning/)
227. [**CAIT: A Syntactic Parsing Toolkit for Child–Adult InTeractions**Francesca Padovani, Xiulin Yang, Bastian Bunzeck, et al.*2026*](https://md.research.elamur.ai/free/en/papers/padovani-2026-cait-syntactic-parsing-toolkit-child/)
228. [**Can LLMs Faithfully Explain Themselves in Low-Resource Languages? A Case Study on Emotion Detection in Persian**Mobina Mehrazar, Mohammad Amin Yousefi, Parisa Beygi, et al.*2026*](https://md.research.elamur.ai/free/en/papers/mehrazar-2026-can-llms-faithfully-explain-themselves/)
229. [**Child-directed speech facilitates production, not comprehension, in BabyLMs**Bastian Bunzeck, Sina Zarrieß*2026*](https://md.research.elamur.ai/free/en/papers/bunzeck-2026-child-directed-speech-facilitates-production/)
230. [**ChildTalk: A Multi-Dialect Chinese Child Speech Corpus with Full-Length Child–Caregiver Conversations for Speech Recognition**Jiaming Zhou, Yujie Guo, Shiwan Zhao, et al.*2026*](https://md.research.elamur.ai/free/en/papers/zhou-2026-childtalk-multi-dialect-chinese-child/)
231. [**Common Sense vs. Morality: The Curious Case of Narrative Focus Bias in LLMs**Saugata Purkayastha, Pranav Kushare, Pragya Paramita Pal, et al.*2026*](https://md.research.elamur.ai/free/en/papers/purkayastha-2026-common-sense-vs-morality-curious/)
232. [**CRiT-QA: Evaluating Multi-hop Reasoning with Counterfactual Chains and Distractor Traps**Jungmin Yun, June Hyoung Kwon, Youngbin Kim*2026*](https://md.research.elamur.ai/free/en/papers/yun-2026-crit-qa-evaluating-multi-hop/)
233. [**Data-Centric Approach at the LoResMT 2026 Turkic Translation Challenge: Russian-Kyrgyz**Dmitry Novokshanov*2026*](https://md.research.elamur.ai/free/en/papers/novokshanov-2026-data-centric-approach-loresmt-2026/)
234. [**Detecting Hallucinations in Authentic LLM–Human Interactions**Yujie Ren, Niklas Gruhlke, Anne Lauscher*2026*](https://md.research.elamur.ai/free/en/papers/ren-2026-detecting-hallucinations-authentic-llm-human/)
235. [**Detecting Latin in Historical Books with Large Language Models: A Multimodal Benchmark**Yu Wu, Ke Shu, Jonas Fischer, et al.*2026*](https://md.research.elamur.ai/free/en/papers/wu-2026-detecting-latin-historical-books-large/)
236. [**DRInQ: Evaluating Conversational Implicature with Controlled Context Variation**Hirona Jacqueline Arai, Xiang Ren*2026*](https://md.research.elamur.ai/free/en/papers/arai-2026-drinq-evaluating-conversational-implicature-controlled/)
237. [**Evaluating the Effect of Question Wording Variations on Answer Consistency in Large Language Models**Junya Takayama, Masaya Ohagi, Tomoya Mizumoto, et al.*2026*](https://md.research.elamur.ai/free/en/papers/takayama-2026-evaluating-effect-question-wording-variations/)
238. [**HEBATRON: A Hebrew-Specialized Open-Weight Mixture-of-Experts Language Model**Noam Kayzer, Dan Revital, Ori Bar Joseph, et al.*2026*](https://md.research.elamur.ai/free/en/papers/kayzer-2026-hebatron-hebrew-specialized-open-weight/)
239. [**HistoryBankQA: Multilingual Temporal Question Answering on Historical Events**Biswadip Mandal, Anant Khandelwal, Manish Gupta*2026*](https://md.research.elamur.ai/free/en/papers/mandal-2026-historybankqa-multilingual-temporal-question-answering/)
240. [**HOTATE: A Japanese Dialogue Corpus Annotated with Responses of Private Thoughts and Public Statements**Yuko Toda, Daisuke Maekawa, Kota Manabe, et al.*2026*](https://md.research.elamur.ai/free/en/papers/toda-2026-hotate-japanese-dialogue-corpus-annotated/)
241. [**HotelCheckSpan: A Benchmark Dataset for LLM Faithfulness**Patricia Schmidtova, Ondrej Dusek, Saad Mahamood*2026*](https://md.research.elamur.ai/free/en/papers/schmidtova-2026-hotelcheckspan-benchmark-dataset-llm-faithfulness/)
242. [**HybridCodeAuthorship: A Benchmark Dataset for Line-Level Code Authorship Detection**Luke S. Patterson, Li Wang, Adam Faulkner*2026*](https://md.research.elamur.ai/free/en/papers/patterson-2026-hybridcodeauthorship-benchmark-dataset-line-level/)
243. [**Identifying Imaging Follow-Up in Radiology Reports: A Comparative Analysis of Traditional ML and LLM Approaches**Namu Park, Giridhar Kaushik Ramachandran, Kevin Lybarger, et al.*2026*](https://md.research.elamur.ai/free/en/papers/park-2026-identifying-imaging-follow-up-radiology/)
244. [**Information Asymmetry across Language Varieties: A Case Study on Cantonese-Mandarin and Bavarian-German QA**Renhao Pei, Siyao Peng, Verena Blaschke, et al.*2026*](https://md.research.elamur.ai/free/en/papers/pei-2026-information-asymmetry-across-language-varieties/)
245. [**InstructSum: A Benchmark to Evaluate Instruction-Following Capability of Large Language Models in Summarization**Kosuke Nishida, Kyosuke Nishida, Itsumi Saito*2026*](https://md.research.elamur.ai/free/en/papers/nishida-2026-instructsum-benchmark-evaluate-instruction-following/)
246. [**Is Biomedical Specialization Still Worth It? Insights from Domain-Adaptive Language Modelling with a New French Health Corpus**Aidan Mannion, Cécile Macaire, Armand Violle, et al.*2026*](https://md.research.elamur.ai/free/en/papers/mannion-2026-is-biomedical-specialization-still-worth/)
247. [**Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages**Noam Dahan, Omer Kidron, Gabriel Stanovsky*2026*](https://md.research.elamur.ai/free/en/papers/dahan-2026-leveraging-digitized-newspapers-collect-summarization/)
248. [**LLMs Out-of-the-Box Do Not Generate Context-Appropriate Word Order in Russian**Alina Shabaeva, John Frederick Bailyn, Owen Rambow*2026*](https://md.research.elamur.ai/free/en/papers/shabaeva-2026-llms-out-box-do-not/)
249. [**Meaning Over Morphology: A Multi-Metric Benchmark of LLMs for Bangla Dialect Translation**Soumik Deb Niloy, Subhey Sadi Rahman, Mahbub E Sobhani, et al.*2026*](https://md.research.elamur.ai/free/en/papers/niloy-2026-meaning-over-morphology-multi-metric/)
250. [**mSCoRe: A Multilingual and Scalable Benchmark for Skill-based Commonsense Reasoning**Nghia Trung Ngo, Franck Dernoncourt, Thien Huu Nguyen*2026*](https://md.research.elamur.ai/free/en/papers/ngo-2026-mscore-multilingual-scalable-benchmark-skill/)
251. [**Multilingual Target-Stance Extraction**Ethan Leigh Mines, Bonnie J. Dorr*2026*](https://md.research.elamur.ai/free/en/papers/mines-2026-multilingual-target-stance-extraction/)
252. [**Multimodal LLMs Do Not Compose Skills Optimally across Modalities**Paula Ontalvilla, Aitor Ormazabal, Gorka Azkune*2026*](https://md.research.elamur.ai/free/en/papers/ontalvilla-2026-multimodal-llms-do-not-compose/)
253. [**NegNLI-BR: A Brazilian Portuguese Benchmark for Negation in Natural Language Inference**Matheus Westhelle, Viviane Moreira*2026*](https://md.research.elamur.ai/free/en/papers/westhelle-2026-negnli-br-brazilian-portuguese-benchmark/)
254. [**On the Robustness of Morphosyntactic Transformation with Large Language Models: The Case of Quechua Collao**Pool Pocco, Arturo Oncevay*2026*](https://md.research.elamur.ai/free/en/papers/pocco-2026-robustness-morphosyntactic-transformation-large-language/)
255. [**Quality and Appropriateness of Large Text Datasets for Irish NLP**Abigail Walsh, Mark Andrade, Jane Lauren Adkins, et al.*2026*](https://md.research.elamur.ai/free/en/papers/walsh-2026-quality-appropriateness-large-text-datasets/)
256. [**RadTimeline: Timeline Summarization for Longitudinal Radiological Lung Findings**Sitong Zhou, Meliha Yetisgen, Mari Ostendorf*2026*](https://md.research.elamur.ai/free/en/papers/zhou-2026-radtimeline-timeline-summarization-longitudinal-radiological/)
257. [**RelEx-PT: A Portuguese Sentence-Level Relation Extraction Dataset**Tomás Pinto, Catarina Silva, Hugo Goncalo Oliveira*2026*](https://md.research.elamur.ai/free/en/papers/pinto-2026-relex-pt-portuguese-sentence-level/)
258. [**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/)
259. [**STAGEET: Stage-wise Typed Edit Tagging for Grammatical Error Correction with Arabic as a Case Study**Wenjie Lou, Alaa Mamdouh Akef*2026*](https://md.research.elamur.ai/free/en/papers/lou-2026-stageet-stage-wise-typed-edit/)
260. [**Stands to Reason: Investigating the Effect of Reasoning on Idiomaticity Detection**Dylan Phelps, Rodrigo Wilkens, Edward Gow-Smith, et al.*2026*](https://md.research.elamur.ai/free/en/papers/phelps-2026-stands-reason-investigating-effect-reasoning/)
261. [**STRUDEL: Unrolling a Benchmark for Evaluating Vision-Language Models on Structured Diagram Understanding across Domains**Daniel Steinigen, Lucie Flek, Sebastian Houben*2026*](https://md.research.elamur.ai/free/en/papers/steinigen-2026-strudel-unrolling-benchmark-evaluating-vision/)
262. [**The Surge of Anti-Semitism in German Social Media following the October 7 Attacks**Gregor Wiedemann, Daniel Wehrend*2026*](https://md.research.elamur.ai/free/en/papers/wiedemann-2026-surge-anti-semitism-german-social/)
263. [**Translation as Augmentation: Effect of Translated Data on Assessment of Difficulty**Yiheng Wu, Jue Hou, Roman Yangarber*2026*](https://md.research.elamur.ai/free/en/papers/wu-2026-translation-as-augmentation-effect-translated/)
264. [**Tree-Based Interview Topic Guidance for Collecting Target Information under Adaptive Topic continuation/switching**Fuminori Nagasawa, Ekai Hashimoto, Shun Shiramatsu*2026*](https://md.research.elamur.ai/free/en/papers/nagasawa-2026-tree-based-interview-topic-guidance/)
265. [**Uncovering Hidden Violent Tendencies in LLMs: A Demographic Analysis via Behavioral Vignettes**Quintin Myers, Yanjun Gao*2026*](https://md.research.elamur.ai/free/en/papers/myers-2026-uncovering-hidden-violent-tendencies-llms/)
266. [**ViX-Ray: A Vietnamese Chest X-Ray Dataset for Vision-Language Models**Duy Vu Minh Nguyen, Chinh Thanh Truong, Trần Hoàng Phúc, et al.*2026*](https://md.research.elamur.ai/free/en/papers/nguyen-2026-vix-ray-vietnamese-chest-x/)
267. [**When Similar Means Different: Evaluating LLMs on Arabic–Hebrew Cognates**Junhong Liang, Noor Abo Mokh, Bashar Alhafni*2026*](https://md.research.elamur.ai/free/en/papers/liang-2026-similar-means-different-evaluating-llms/)
268. [**Why do Large Language Models Fail in Low-resource Translation? Unraveling the Token Dynamics of Large Language Models for Machine Translation**Shenbin Qian, Yves Scherrer*2026*](https://md.research.elamur.ai/free/en/papers/qian-2026-why-do-large-language-models/)
269. [**MuDRiC: Multi-Dialect Reasoning for Arabic Commonsense Validation**Kareem Elozeiri, Mervat Abassy, Preslav Nakov, et al.*2025*](https://md.research.elamur.ai/free/en/papers/elozeiri-2025-mudric-multi-dialect-reasoning-arabic/)
270. [**Sarc7: Evaluating Sarcasm Detection and Generation with Seven Types and Emotion-Informed Techniques**Lang Xiong, Raina Gao, Alyssa Jeong*2025*](https://md.research.elamur.ai/free/en/papers/xiong-2025-sarc7-evaluating-sarcasm-detection-generation/)
271. [**On the importance of Data Scale in Pretraining Arabic Language Models**Abbas Ghaddar, Philippe Langlais, Mehdi Rezagholizadeh, et al.*2024*](https://md.research.elamur.ai/free/en/papers/ghaddar-2024-importance-data-scale-pretraining-arabic/)
272. [**Questions After ChatGPT: Opportunities and Challenges of Very Large Pre-trained Models (问答ChatGPT之后: 超大预训练模型的机遇和挑战)**Jingwei Lu (卢经纬), Chao Guo (郭超), Xingyuan Dai (戴星原), et al.*2023*](https://md.research.elamur.ai/free/en/papers/lu-2023-questions-after-chatgpt/)

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