---
title: "Who cites Ruty Rinott — page 2"
description: "1,287 papers of the Elamur Research library cite the works of Ruty Rinott, found in their bibliographies · page 2 of 2 · 101–127; the most cited first."
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# Who cites Ruty Rinott

1,461 citations of their works from 1,287 papers of our library (of the 140,884 whose bibliographies we read) · 127 with a page here, the most cited first · page 2 of 2 · 101–127 · [← Ruty Rinott](https://md.research.elamur.ai/free/en/authors/ruty-rinott/)

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

101. [**Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing**Jaehun Jung, Peter West, Liwei Jiang, et al.*2023 · 33 citations*](https://md.research.elamur.ai/free/en/papers/jung-2023-impossible-distillation-low-quality-model/)
102. [**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/)
103. [**TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages**Anirudh Srinivasan, Eunsol Choi*2022 · 29 citations*](https://md.research.elamur.ai/free/en/papers/srinivasan-2022-tydip-dataset-politeness-classification-nine/)
104. [**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/)
105. [**Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection**Erik Arakelyan, Arnav Arora, Isabelle Augenstein*2023 · 19 citations*](https://md.research.elamur.ai/free/en/papers/arakelyan-2023-topic-guided-sampling-data-efficient/)
106. [**Comparative Study of Multilingual Idioms and Similes in Large Language Models**Paria Khoshtab, Danial Namazifard, Mostafa Masoudi, et al.*2025 · 14 citations*](https://md.research.elamur.ai/free/en/papers/khoshtab-2025-comparative-study-multilingual-idioms-similes/)
107. [**Investigating the Performance of Transformer-Based NLI Models on Presuppositional Inferences**Jad Kabbara, Jackie Chi Kit Cheung*2022 · 12 citations*](https://md.research.elamur.ai/free/en/papers/kabbara-2022-investigating-performance-transformer-based-nli/)
108. [**AmQA: Amharic Question Answering Dataset**Tilahun Abedissa, Ricardo Usbeck, Yaregal Assabie*2023 · 10 citations*](https://md.research.elamur.ai/free/en/papers/abedissa-2023-amqa-amharic-question-answering-dataset/)
109. [**Plumeria at SemEval-2022 Task 6: Sarcasm Detection for English and Arabic Using Transformers and Data Augmentation**Mosab Shaheen, Shubham Kumar Nigam*2022 · 10 citations*](https://md.research.elamur.ai/free/en/papers/shaheen-2022-plumeria-semeval-2022-task-6/)
110. [**DialectalArabicMMLU: Benchmarking Dialectal Capabilities in Arabic and Multilingual Language Models**Malik H. Altakrori, Nizar Habash, Teresa Lynn, et al.*2026 · 9 citations*](https://md.research.elamur.ai/free/en/papers/altakrori-2026-dialectalarabicmmlu-benchmarking-dialectal-capabilities-arabic/)
111. [**SemEval-2022 Task 3: PreTENS — Evaluating Neural Networks on Presuppositional Semantic Knowledge**Roberto Zamparelli, Shammur Chowdhury, Dominique Brunato, et al.*2022 · 8 citations*](https://md.research.elamur.ai/free/en/papers/zamparelli-2022-semeval-2022-task-3-pretens/)
112. [**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/)
113. [**MultiWikiQA: A Reading Comprehension Benchmark in 300+ Languages**Dan Saattrup Smart*2026 · 6 citations*](https://md.research.elamur.ai/free/en/papers/smart-2026-multiwikiqa-reading-comprehension-benchmark-300/)
114. [**Munazarat 1.0: A Corpus of Arabic Competitive Debates**Mohammad M. Khader, AbdulGabbar Al-Sharafi, Mohamad Hamza Al-Sioufy, et al.*2024 · 5 citations*](https://md.research.elamur.ai/free/en/papers/khader-2024-munazarat-1-0-corpus-arabic/)
115. [**Plumeria at SemEval-2022 Task 6: Robust Approaches for Sarcasm Detection for English and Arabic Using Transformers and Data Augmentation**Shubham Kumar Nigam, Mosab Shaheen*2022 · 5 citations*](https://md.research.elamur.ai/free/en/papers/nigam-2022-plumeria-semeval-2022-task-6/)
116. [**AmaSQuAD: A Benchmark for Amharic Extractive Question Answering**Nebiyou Daniel Hailemariam, Blessed Guda, Tsegazeab Tefferi*2025 · 2 citations*](https://md.research.elamur.ai/free/en/papers/hailemariam-2025-amasquad-benchmark-amharic-extractive-question/)
117. [**Improving Natural Language Inference in Arabic using Transformer Models and Linguistically Informed Pre-Training**Mohammad Majd Saad Al Deen, Maren Pielka, Jorn Hees, et al.*2023 · 2 citations*](https://md.research.elamur.ai/free/en/papers/deen-2023-improving-natural-language-inference-arabic/)
118. [**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/)
119. [**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/)
120. [**Harnessing Multilingual Resources to Question Answering in Arabic**Khalid Alnajjar, Mika Hamalainen*2022 · 1 citation*](https://md.research.elamur.ai/free/en/papers/alnajjar-2022-harnessing-multilingual-resources-question-answering/)
121. [**Diagnosing Translated Benchmarks: An Automated Quality Assurance Study of the EU20 Benchmark Suite**Klaudia Thellmann, Bernhard Stadler, Michael Färber*2026*](https://md.research.elamur.ai/free/en/papers/thellmann-2026-diagnosing-translated-benchmarks-automated-quality/)
122. [**E-CONAN (Entailment, CONtradition And Neutral) Benchmarks: Arabic Textual Entailment and Natural Inference Datasets**Khloud AL Jallad, Nada Ghneim, Ghaida Rebdawi*2026*](https://md.research.elamur.ai/free/en/papers/jallad-2026-e-conan-entailment-contradition-neutral-benchmarks/)
123. [**E-CONAN (Entailment, CONtradition And Neutral) Diagnostics Dataset Investigating Linguistic Phenomena in Arabic Natural Language Understanding**Khloud AL Jallad, Nada Ghneim, Ghaida Rebdawi*2026*](https://md.research.elamur.ai/free/en/papers/jallad-2026-e-conan-entailment-contradition-neutral/)
124. [**FIBER: A Multilingual Evaluation Resource for Factual Inference Bias**Evren Ayberk Munis, Deniz Yilmaz, Arianna Muti, et al.*2026*](https://md.research.elamur.ai/free/en/papers/munis-2026-fiber-multilingual-evaluation-resource-factual/)
125. [**I Understand How You Feel: Enhancing Deeper Emotional Support Through Multilingual Emotional Validation in Dialogue System**Zi Haur Pang, Yahui Fu, Koji Inoue, et al.*2026*](https://md.research.elamur.ai/free/en/papers/pang-2026-i-understand-how-you-feel/)
126. [**Quantifying the Impact of Translation Errors on Multilingual LLM Evaluation**Klaudia Thellmann, Bernhard Stadler, Michael Färber, et al.*2026*](https://md.research.elamur.ai/free/en/papers/thellmann-2026-quantifying-impact-translation-errors-multilingual/)
127. [**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/)

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