---
title: "Who cites Niklas Muennighoff — page 3"
description: "4,552 papers of the Elamur Research library cite the works of Niklas Muennighoff, found in their bibliographies · page 3 of 4 · 201–300; the most cited…"
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---
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# Who cites Niklas Muennighoff

5,652 citations of their works from 4,552 papers of our library (of the 140,884 whose bibliographies we read) · 301 with a page here, the most cited first · page 3 of 4 · 201–300 · [← Niklas Muennighoff](https://md.research.elamur.ai/free/en/authors/niklas-muennighoff/)

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201. [**Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies**Chaofan Tao, Qian Liu, Longxu Dou, et al.*2024 · 42 citations*](https://md.research.elamur.ai/free/en/papers/tao-2024-scaling-laws-vocabulary-larger-models/)
202. [**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/)
203. [**Towards Effective Disambiguation for Machine Translation with Large Language Models**Vivek Iyer, Pinzhen Chen, Alexandra Birch*2023 · 38 citations*](https://md.research.elamur.ai/free/en/papers/iyer-2023-effective-disambiguation-machine-translation-large/)
204. [**Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs**Nicolas Boizard, Kevin El Haddad, Celine Hudelot, et al.*2024 · 34 citations*](https://md.research.elamur.ai/free/en/papers/boizard-2024-towards-cross-tokenizer-distillation-universal/)
205. [**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/)
206. [**BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidence**Jiajie Jin, Yutao Zhu, Yujia Zhou, et al.*2024 · 28 citations*](https://md.research.elamur.ai/free/en/papers/jin-2024-bider-bridging-knowledge-inconsistency-efficient/)
207. [**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/)
208. [**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/)
209. [**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/)
210. [**Taqyim: Evaluating Arabic NLP Tasks Using ChatGPT Models**Zaid Alyafeai, Maged S. Alshaibani, Badr AlKhamissi, et al.*2023 · 25 citations*](https://md.research.elamur.ai/free/en/papers/alyafeai-2023-taqyim-evaluating-arabic-nlp-tasks/)
211. [**Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations**Alexander Hägele, Elie Bakouch, Atli Kosson, et al.*2024 · 24 citations*](https://md.research.elamur.ai/free/en/papers/hagele-2024-scaling-laws-compute-optimal-training/)
212. [**Distillation Scaling Laws**Dan Busbridge, Amitis Shidani, Floris Weers, et al.*2025 · 23 citations*](https://md.research.elamur.ai/free/en/papers/busbridge-2025-distillation-scaling-laws/)
213. [**Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language Models**Yeeun Kim, Youngrok Choi, Eunkyung Choi, et al.*2024 · 22 citations*](https://md.research.elamur.ai/free/en/papers/kim-2024-developing-pragmatic-benchmark-assessing-korean/)
214. [**LLM-Assisted Rule Based Machine Translation for Low/No-Resource Languages**Jared Coleman, Bhaskar Krishnamachari, Ruben Rosales, et al.*2024 · 20 citations*](https://md.research.elamur.ai/free/en/papers/coleman-2024-llm-assisted-rule-based-machine/)
215. [**Multilingual and Explainable Text Detoxification with Parallel Corpora**Daryna Dementieva, Nikolay Babakov, Amit Ronen, et al.*2025 · 19 citations*](https://md.research.elamur.ai/free/en/papers/dementieva-2025-multilingual-explainable-text-detoxification-parallel/)
216. [**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/)
217. [**CamelEval: Advancing Culturally Aligned Arabic Language Models and Benchmarks**Zhaozhi Qian, Faroq Altam, Muhammad Alqurishi, et al.*2024 · 18 citations*](https://md.research.elamur.ai/free/en/papers/qian-2024-cameleval-advancing-culturally-aligned-arabic/)
218. [**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/)
219. [**MultiPragEval: Multilingual Pragmatic Evaluation of Large Language Models**Dojun Park, Jiwoo Lee, Seohyun Park, et al.*2024 · 17 citations*](https://md.research.elamur.ai/free/en/papers/park-2024-multiprageval-multilingual-pragmatic-evaluation-large/)
220. [**METAL: A Multi-Agent Framework for Chart Generation with Test-Time Scaling**Bingxuan Li, Yiwei Wang, Jiuxiang Gu, et al.*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-metal-multi-agent-framework-chart/)
221. [**AraSTEM: A Native Arabic Multiple Choice Question Benchmark for Evaluating LLMs Knowledge In STEM Subjects**Ahmad Mustapha; Hadi Al-Khansa; Hadi Al-Mubasher; Aya Mourad; Ranam Hamoud; Hasan El-Husseini; Marwah Al-Sakkaf;, Mariette Awad*2024 · 15 citations*](https://md.research.elamur.ai/free/en/papers/mustapha-2024-arastem-native-arabic-multiple-choice/)
222. [**Jais 2: A Family of Arabic-Centric Open Large Language Models**Mohamed Anwar, Abed Alhakim Freihat, George Ibrahim, et al.*2026 · 14 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2026-jais-2-family-arabic-centric/)
223. [**Resolving Discrepancies in Compute-Optimal Scaling of Language Models**Tomer Porian, Mitchell Wortsman, Jenia Jitsev, et al.*2024 · 14 citations*](https://md.research.elamur.ai/free/en/papers/porian-2024-resolving-discrepancies-compute-optimal-scaling/)
224. [**101 Billion Arabic Words Dataset**Manel Aloui, Hasna Chouikhi, Ghaith Chaabane, et al.*2024 · 13 citations*](https://md.research.elamur.ai/free/en/papers/aloui-2024-101-billion-arabic-words-dataset/)
225. [**AIN: The Arabic INclusive Large Multimodal Model**Ahmed Heakl, Sara Ghaboura, Omkar Thawkar, et al.*2025 · 11 citations*](https://md.research.elamur.ai/free/en/papers/heakl-2025-ain-arabic-inclusive-large-multimodal/)
226. [**GrammaMT: Improving Machine Translation with Grammar-Informed In-Context Learning**Rita Ramos, Everlyn Asiko Chimoto, Maartje Ter Hoeve, et al.*2025 · 11 citations*](https://md.research.elamur.ai/free/en/papers/ramos-2025-grammamt-improving-machine-translation-grammar/)
227. [**How well can LLMs Grade Essays in Arabic?**Rayed Ghazawi, Edwin Simpson*2025 · 11 citations*](https://md.research.elamur.ai/free/en/papers/ghazawi-2025-well-llms-grade-essays-arabic/)
228. [**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/)
229. [**Do Large Language Models Understand Conversational Implicature- A case study with a Chinese sitcom**Shisen Yue, Siyuan Song, Xinyuan Cheng, et al.*2024 · 11 citations*](https://md.research.elamur.ai/free/en/papers/yue-2024-do-large-language-models-understand/)
230. [**Pragmatic Competence Evaluation of Large Language Models for the Korean Language**Dojun Park, Jiwoo Lee, Hyeyun Jeong, et al.*2024 · 11 citations*](https://md.research.elamur.ai/free/en/papers/park-2024-pragmatic-competence-evaluation-large-language/)
231. [**Bridging the Resource Gap: Exploring the Efficacy of English and Multilingual LLMs for Swedish**Oskar Holmström, Jenny Kunz, Marco Kuhlmann*2023 · 8 citations*](https://md.research.elamur.ai/free/en/papers/holmstrom-2023-bridging-resource-gap-exploring-efficacy/)
232. [**New Encoders for German Trained from Scratch: Comparing ModernGBERT with Converted LLM2Vec Models**Julia Wunderle, Anton Ehrmanntraut, Jan Pfister, et al.*2026 · 7 citations*](https://md.research.elamur.ai/free/en/papers/wunderle-2026-new-encoders-german-trained-scratch/)
233. [**GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training**Omer Nacar, Anis Koubaa, Serry Sibaee, et al.*2025 · 7 citations*](https://md.research.elamur.ai/free/en/papers/nacar-2025-gate-general-arabic-text-embedding/)
234. [**Bilingual Adaptation of Monolingual Foundation Models**Gurpreet Gosal, Yishi Xu, Gokul Ramakrishnan, et al.*2024 · 7 citations*](https://md.research.elamur.ai/free/en/papers/gosal-2024-bilingual-adaptation-monolingual-foundation-models/)
235. [**Arabic Stable LM: Adapting Stable LM 2 1.6B to Arabic**Zaid Alyafeai, Michael Pieler, Hannah Teufel, et al.*2024 · 6 citations*](https://md.research.elamur.ai/free/en/papers/alyafeai-2024-arabic-stable-lm-adapting-stable/)
236. [**Kuwain 1.5B: An Arabic SLM via Language Injection**Khalil Hennara, Sara Chrouf, Mohamed Motaism Hamed, et al.*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/hennara-2025-kuwain-1-5b-arabic-slm/)
237. [**ALADAN at IWSLT24 Low-resource Arabic Dialectal Speech Translation Task**Waad Ben Kheder, Josef Jon, André Beyer, et al.*2024 · 4 citations*](https://md.research.elamur.ai/free/en/papers/kheder-2024-aladan-iwslt24-low-resource-arabic/)
238. [**Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders**Yuwei Zhang, Siffi Singh, Sailik Sengupta, et al.*2024 · 4 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-can-your-model-tell-negation/)
239. [**GemmAr: Enhancing LLMs Through Arabic Instruction-Tuning**Hasna Chouikhi, Manel Aloui, Cyrine Ben Hammou, et al.*2024 · 4 citations*](https://md.research.elamur.ai/free/en/papers/chouikhi-2024-gemmar-enhancing-llms-through-arabic/)
240. [**Beyond MCQ: An Open-Ended Arabic Cultural QA Benchmark with Dialect Variants**Hunzalah Hassan Bhatti, Firoj Alam*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/bhatti-2026-beyond-mcq-open-ended-arabic/)
241. [**Estonian WinoGrande Dataset: Comparative Analysis of LLM Performance on Human and Machine Translation**Marii Ojastu, Hele-Andra Kuulmets, Aleksei Dorkin, et al.*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/ojastu-2026-estonian-winogrande-dataset-comparative-analysis/)
242. [**Scaling Laws for Code: A More Data-Hungry Regime**Xianzhen Luo, Wenzhen Zheng, Qingfu Zhu, et al.*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/luo-2026-scaling-laws-code-more-data/)
243. [**Arabic Multimodal Machine Learning: Datasets, Applications, Approaches, and Challenges**Abdelhamid Haouhat, Slimane Bellaouar, Attia Nehar, et al.*2025 · 3 citations*](https://md.research.elamur.ai/free/en/papers/haouhat-2025-arabic-multimodal-machine-learning-datasets/)
244. [**Natural Language Processing for Tigrinya: Current State and Future Directions**Fitsum Gaim, Jong C. Park*2025 · 3 citations*](https://md.research.elamur.ai/free/en/papers/gaim-2025-natural-language-processing-tigrinya-current/)
245. [**HistoriQA-ThirdRepublic: Multi-Hop Question Answering Corpus for Historical Research, Parliamentary Debates from the French Third Republic (1870-1940)**Aurelien Pellet, Marie Anna Puren, Julien PEREZ*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/pellet-2026-historiqa-thirdrepublic-multi-hop-question/)
246. [**UrBLiMP: A Benchmark for Evaluating the Linguistic Competence of Large Language Models in Urdu**Farah Adeeba, Brian Dillon, Hassan Sajjad, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/adeeba-2026-urblimp-benchmark-evaluating-linguistic-competence/)
247. [**When Flores Bloomz Wrong: Cross-Direction Contamination in Machine Translation Evaluation**David Tan, Pinzhen Chen, Josef van Genabith, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/tan-2026-when-flores-bloomz-wrong-cross/)
248. [**Optimizing RAG Pipelines for Arabic: A Systematic Analysis of Core Components**Jumana Alsubhi, Mohammad D. Alahmadi, Ahmed Alhusayni, et al.*2025 · 2 citations*](https://md.research.elamur.ai/free/en/papers/alsubhi-2025-optimizing-rag-pipelines-arabic-systematic/)
249. [**Transforming Causal LLM into MLM Encoder for Detecting Social Media Manipulation in Telegram**Anton Bazdyrev, Ivan Bashtovyi, Ivan Havlytskyi, et al.*2025 · 2 citations*](https://md.research.elamur.ai/free/en/papers/bazdyrev-2025-transforming-causal-llm-into-mlm/)
250. [**Normalizing without Modernizing: Keeping Historical Wordforms of Middle French while Reducing Spelling Variants**Raphael Rubino, Johanna Gerlach, Jonathan Mutal, et al.*2024 · 2 citations*](https://md.research.elamur.ai/free/en/papers/rubino-2024-normalizing-without-modernizing-keeping-historical/)
251. [**A Massive Scale Semantic Similarity Dataset of Historical English**Emily Silcock, Melissa Dell*2023 · 2 citations*](https://md.research.elamur.ai/free/en/papers/silcock-2023-massive-scale-semantic-similarity-dataset/)
252. [**AMIYA Shared Task: Arabic Modeling In Your Accent at VarDial 2026**Nathaniel R. Robinson, Shahd Abdelmoneim, Anjali Kantharuban, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/robinson-2026-amiya-shared-task-arabic-modeling/)
253. [**Bridging the Culture Gap: A Framework for LLM-Driven Socio-Cultural Localization of Math Word Problems in Low-Resource Languages**Israel Abebe Azime, Tadesse Destaw Belay, Dietrich Klakow, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/azime-2026-bridging-culture-gap-framework-llm/)
254. [**BSC's Submission to the Instruction Following Track of IWSLT 2026**Oriol Pareras, Joan Llado, Pol Buitrago, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/pareras-2026-bsc-s-submission-instruction-following/)
255. [**Can Linguistically Related Languages Guide LLM Translation in Low-Resource Settings?**Aishwarya Ramasethu, Rohin Garg, Niyathi Allu, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/ramasethu-2026-can-linguistically-related-languages-guide/)
256. [**CEFR-Cymraeg: A Dataset and Baseline Models for Language Proficiency Assessment in Welsh**Eeshan Waqar, Jonathan Davies, Dawn Knight, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/waqar-2026-cefr-cymraeg-dataset-baseline-models/)
257. [**Context Volume Drives Performance: Tackling Domain Shift in Extremely Low-Resource Translation via RAG**David Samuel Setiawan, Raphaël Merx, Jey Han Lau*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/setiawan-2026-context-volume-drives-performance-tackling/)
258. [**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/)
259. [**HPLT 3.0: Very Large-Scale Multilingual Resources for LLMs and MT. Mono- and Bi-lingual Data, Multilingual Evaluation, and Pre-Trained Models**Stephan Oepen, Nikolay Arefyev, Mikko Aulamo, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/oepen-2026-hplt-3-0-very-large/)
260. [**Reformulate and Create, Don't Translate: Creating Natural Prompts for Underserved Languages**Annika Simonsen, Mathias Stenlund, Lars Bungum, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/simonsen-2026-reformulate-create-don-t-translate/)
261. [**SynthLLM: An LLM-based Scalable Synthetic Data Generation Pipeline for Low-Resource Languages**Solmaz Panahi, Vasudevan Nedumpozhimana, John Kelleher*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/panahi-2026-synthllm-llm-based-scalable-synthetic/)
262. [**Tao–Filipino Neural Machine Translation: Strategies for Ultra–Low-Resource Settings**Adrian Denzel Macayan, Luis Andrew Sunga Madridijo, Ellexandrei Esponilla, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/macayan-2026-tao-filipino-neural-machine-translation/)
263. [**Bridging the Socioeconomic Gap in Education: A Hybrid AI and Human Annotation Approach**Nahed Abdelgaber, Labiba Jahan, Arham Vinit Doshi, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/abdelgaber-2025-bridging-socioeconomic-gap-education-hybrid/)
264. [**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/)
265. [**A Recipe for Adapting Multilingual Embedders to OCR-Error Robustness and Historical Texts**Andrianos Michail, Stylianos Psychias, Juri Opitz, et al.*2026*](https://md.research.elamur.ai/free/en/papers/michail-2026-recipe-adapting-multilingual-embedders-ocr/)
266. [**Align and Shine: Building High-quality Sentence-aligned Corpora for Multilingual Text Simplification**Luis Kenji Hilasaca Sanchez, Nouran Khallaf, Serge Sharoff*2026*](https://md.research.elamur.ai/free/en/papers/sanchez-2026-align-shine-building-high-quality/)
267. [**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/)
268. [**Comparing Approaches to Automatic Summarization in Less-Resourced Languages**Chester Palen-Michel, Constantine Lignos*2026*](https://md.research.elamur.ai/free/en/papers/michel-2026-comparing-approaches-automatic-summarization-less/)
269. [**Corpora Generation for Urdu Grammatical Error Correction**Syed Ahad, Burhanuddin Aliasghar Ezzi, Muhammad Arsalan Hussain, et al.*2026*](https://md.research.elamur.ai/free/en/papers/ahad-2026-corpora-generation-urdu-grammatical-error/)
270. [**DAMETA: An LLM Benchmark for Danish Metaphor Interpretation with Systematically Varied Distractors**Nina Skovgaard Schneidermann, Sanni Nimb, Nathalie Carmen Hau Norman, et al.*2026*](https://md.research.elamur.ai/free/en/papers/schneidermann-2026-dameta-llm-benchmark-danish-metaphor/)
271. [**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/)
272. [**DeepICD-R1: Medical Reasoning through Hierarchical Rewards and Unsupervised Distillation**Tom Röhr, Thomas Maximilian Josef Steffek, Roman Teucher, et al.*2026*](https://md.research.elamur.ai/free/en/papers/rohr-2026-deepicd-r1-medical-reasoning-through/)
273. [**Dialectal Filtering: Synthesizing Kurdish Corpora for Low-Resource Varieties by Utilizing “Noise” in Large Textual Data**Christian Schuler, Raman Ahmad, Ānrán Wáng, et al.*2026*](https://md.research.elamur.ai/free/en/papers/schuler-2026-dialectal-filtering-synthesizing-kurdish-corpora/)
274. [**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/)
275. [**From Noise to Signal: When Outliers Seed New Topics**Evangelia Zve, Gauvain Bourgne, Benjamin Icard, et al.*2026*](https://md.research.elamur.ai/free/en/papers/zve-2026-noise-signal-when-outliers-seed/)
276. [**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/)
277. [**I Came, I Saw, I Explained: Benchmarking Multimodal LLMs on Figurative Meaning in Memes**Shijia Zhou, Saif M. Mohammad, Barbara Plank, et al.*2026*](https://md.research.elamur.ai/free/en/papers/zhou-2026-i-came-i-saw-i/)
278. [**Measuring Idiomaticity in Text Embedding Models with epsilon-compositionality**Sondre Wold, Étienne Simon, Erik Velldal, et al.*2026*](https://md.research.elamur.ai/free/en/papers/wold-2026-measuring-idiomaticity-text-embedding-models/)
279. [**Merge and Conquer: Instructing Multilingual Models by Adding Target Language Weights**Eneko Valero, Maria Ribalta i Albado, Oscar Sainz, et al.*2026*](https://md.research.elamur.ai/free/en/papers/valero-2026-merge-conquer-instructing-multilingual-models/)
280. [**MEUR: A Benchmark for Evaluating Vision-Language Models on Multimodal Event Understanding and Reasoning**Zimu Wang, Yuqi Wang, Tong Chen, et al.*2026*](https://md.research.elamur.ai/free/en/papers/wang-2026-meur-benchmark-evaluating-vision-language/)
281. [**Modeling semantic association in self-paced reading with language model embeddings**Sara Møller Østergaard, Kenneth Enevoldsen, Afra Alishahi, et al.*2026*](https://md.research.elamur.ai/free/en/papers/ostergaard-2026-modeling-semantic-association-self-paced/)
282. [**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/)
283. [**MzansiText and MzansiLM: An Open Corpus and Decoder-Only Language Model for South African Languages**Anri M. Lombard, Simbarashe Mawere, Temi Aina, et al.*2026*](https://md.research.elamur.ai/free/en/papers/lombard-2026-mzansitext-mzansilm-open-corpus-decoder/)
284. [**One Word Is Not Enough: Simple Prompts Improve Word Embeddings**Rajeev Ranjan*2026*](https://md.research.elamur.ai/free/en/papers/ranjan-2026-one-word-is-not-enough/)
285. [**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/)
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