---
title: "Who cites Arash Ahmadian — 429 papers of our library"
description: "429 papers of the Elamur Research library cite the works of Arash Ahmadian, found in their bibliographies; the most cited first."
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---
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# Who cites Arash Ahmadian

436 citations of their works from 429 papers of our library (of the 141,840 whose bibliographies we read) · 31 with a page here, the most cited first · [← Arash Ahmadian](https://md.research.elamur.ai/free/en/authors/arash-ahmadian/)

1. [**Kimi k1.5: Scaling Reinforcement Learning with LLMs arXiv:2501.12599**Kimi Team, Angang Du, Bofei Gao, et al.*2025 · 413 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-k1-5-scaling-reinforcement/)
2. [**Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning arXiv:2503.09516**Bowen Jin, Hansi Zeng, Zhenrui Yue, et al.*2025 · 303 citations*](https://md.research.elamur.ai/free/en/papers/jin-2025-search-r1-training-llms-reason/)
3. [**Understanding R1-Zero-Like Training: A Critical Perspective arXiv:2503.20783**Zichen Liu, Changyu Chen, Wenjun Li, et al.*2025 · 266 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-understanding-r1-zero-like-training/)
4. [**Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey arXiv:2403.14608**Zeyu Han, Chao Gao, Jinyang Liu, et al.*2024 · 236 citations*](https://md.research.elamur.ai/free/en/papers/han-2024-parameter-efficient-fine-tuning-large/)
5. [**Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model? arXiv:2504.13837 · NeurIPS 2025 Oral; ICML 2025 AI4MATH workshop best paper**Yang Yue, Zhiqi Chen, Rui Lu, et al.*2025 · 196 citations*](https://md.research.elamur.ai/free/en/papers/yue-2025-reinforcement-learning-really-incentivize-reasoning/)
6. [**Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier arXiv:2412.04261**John Dang, Shivalika Singh, Daniel D'souza, et al.*2024 · 180 citations*](https://md.research.elamur.ai/free/en/papers/dang-2024-aya-expanse-combining-research-breakthroughs/) **[Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation](https://md.research.elamur.ai/free/en/papers/singh-2025-global-mmlu-understanding-addressing-cultural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shivalika Singh, Angelika Romanou, Clémentine Fourrier, et al.*2025 · 179 citations* [**Training Language Models to Self-Correct via Reinforcement Learning arXiv:2409.12917**Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, et al.*2024 · 151 citations*](https://md.research.elamur.ai/free/en/papers/kumar-2024-training-language-models-self-correct/) [**Process Reinforcement through Implicit Rewards arXiv:2502.01456**Ganqu Cui, Lifan Yuan, Zefan Wang, et al.*2025 · 147 citations*](https://md.research.elamur.ai/free/en/papers/cui-2025-process-reinforcement-through-implicit-rewards/) [**Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs arXiv:2503.01307**Kanishk Gandhi, Ayush Chakravarthy, Anikait Singh, et al.*2025 · 110 citations*](https://md.research.elamur.ai/free/en/papers/gandhi-2025-cognitive-behaviors-that-enable-self/) **[Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art](https://md.research.elamur.ai/free/en/papers/liu-2025-culturally-aware-adapted-nlp-taxonomy/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Chen Cecilia Liu, Iryna Gurevych, Anna Korhonen*2025 · 89 citations* [**Llama-Nemotron: Efficient Reasoning Models arXiv:2505.00949**Akhiad Bercovich, Itay Levy, Izik Golan, et al.*2025 · 75 citations*](https://md.research.elamur.ai/free/en/papers/bercovich-2025-llama-nemotron-efficient-reasoning-models/) [**INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge arXiv:2411.19799**Angelika Romanou, Negar Foroutan, Anna Sotnikova, et al.*2024 · 73 citations*](https://md.research.elamur.ai/free/en/papers/romanou-2024-include-evaluating-multilingual-language-understanding/) [**Seed-TTS: A Family of High-Quality Versatile Speech Generation Models arXiv:2406.02430**Philip Anastassiou, Jiawei Chen, Jitong Chen, et al.*2024 · 69 citations*](https://md.research.elamur.ai/free/en/papers/anastassiou-2024-seed-tts-family-high/) **[TurBLiMP: A Turkish Benchmark of Linguistic Minimal Pairs](https://md.research.elamur.ai/free/en/papers/basar-2025-turblimp-turkish-benchmark-linguistic-minimal/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ezgi Başar, Francesca Padovani, Jaap Jumelet, et al.*2025 · 14 citations* [**From Guidelines to Practice: A New Paradigm for Arabic Language Model Evaluation arXiv:2506.01920**Serry Sibaee, Omer Nacar, Adel Ammar, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/sibaee-2025-guidelines-practice-new-paradigm-arabic/) **[Sacred or Synthetic? Evaluating LLM Reliability and Abstention for Religious Questions](https://md.research.elamur.ai/free/en/papers/atif-2025-sacred-synthetic-evaluating-llm-reliability/) [Proceedings of the AAAI/ACM Conference on AI Ethics and Society](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-acm-conference-on-ai-ethics-and-society/)**Farah Atif, Nursultan Askarbekuly, Kareem Darwish, et al.*2025 · 8 citations* **[Fine-Grained Reward Optimization for Machine Translation using Error Severity Mappings](https://md.research.elamur.ai/free/en/papers/ramos-2026-fine-grained-reward-optimization-machine/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Miguel Moura Ramos, Tomás Almeida, Daniel Vareta, et al.*2026 · 7 citations* **[Estonian WinoGrande Dataset: Comparative Analysis of LLM Performance on Human and Machine Translation](https://md.research.elamur.ai/free/en/papers/ojastu-2026-estonian-winogrande-dataset-comparative-analysis/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Marii Ojastu, Hele-Andra Kuulmets, Aleksei Dorkin, et al.*2026 · 3 citations* **[When Flores Bloomz Wrong: Cross-Direction Contamination in Machine Translation Evaluation](https://md.research.elamur.ai/free/en/papers/tan-2026-when-flores-bloomz-wrong-cross/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**David Tan, Pinzhen Chen, Josef van Genabith, et al.*2026 · 2 citations* **[Transforming Causal LLM into MLM Encoder for Detecting Social Media Manipulation in Telegram](https://md.research.elamur.ai/free/en/papers/bazdyrev-2025-transforming-causal-llm-into-mlm/) [Ukrainian Natural Language Processing Workshop](https://md.research.elamur.ai/free/en/venues/unlp/)**Anton Bazdyrev, Ivan Bashtovyi, Ivan Havlytskyi, et al.*2025 · 2 citations* **[AMIYA Shared Task: Arabic Modeling In Your Accent at VarDial 2026](https://md.research.elamur.ai/free/en/papers/robinson-2026-amiya-shared-task-arabic-modeling/) [Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)](https://md.research.elamur.ai/free/en/venues/vardial/)**Nathaniel R. Robinson, Shahd Abdelmoneim, Anjali Kantharuban, et al.*2026 · 1 citation* **[Bridging the Culture Gap: A Framework for LLM-Driven Socio-Cultural Localization of Math Word Problems in Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/azime-2026-bridging-culture-gap-framework-llm/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Israel Abebe Azime, Tadesse Destaw Belay, Dietrich Klakow, et al.*2026 · 1 citation* [**When Alignment Hurts: Decoupling Representational Spaces in Multilingual Models arXiv:2508.12803**Ahmed Elshabrawy, Hour Kaing, Haiyue Song, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/elshabrawy-2025-alignment-hurts-decoupling-representational-spaces/) **[AnswerCarefully: Creating a Dataset for LLM Safety in Japanese](https://md.research.elamur.ai/free/en/papers/suzuki-2026-answercarefully-creating-dataset-llm-safety/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Hisami Suzuki, Satoru Katsumata, Takashi Kodama, et al.*2026* [**Arabic Safety Alignment as Selective Refusal: An Empirical Study of SFT, DPO, and Guard Calibration arXiv:2608.29378**Mohamad Zbib, Ammar Mohanna*2026*](https://md.research.elamur.ai/free/en/papers/zbib-2026-arabic-safety-alignment-selective-refusal/) [**Bridging the English-Arabic Medical Knowledge Gap: Targeted Low-Rank Adaptation via Causal Layer Selection arXiv:2608.00207 · Findings of the Association for Computational Linguistics: EMNLP 2026**Chaimae Abouzahir, Musa Khan, Hala Ali-Hassan, et al.*2026*](https://md.research.elamur.ai/free/en/papers/abouzahir-2026-bridging-english-arabic-medical-knowledge/) **[Comparing Approaches to Automatic Summarization in Less-Resourced Languages](https://md.research.elamur.ai/free/en/papers/michel-2026-comparing-approaches-automatic-summarization-less/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Chester Palen-Michel, Constantine Lignos*2026* **[mSCoRe: A Multilingual and Scalable Benchmark for Skill-based Commonsense Reasoning](https://md.research.elamur.ai/free/en/papers/ngo-2026-mscore-multilingual-scalable-benchmark-skill/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Nghia Trung Ngo, Franck Dernoncourt, Thien Huu Nguyen*2026* **[SiniticMTError: A Machine Translation Dataset with Error Annotations for Sinitic Languages](https://md.research.elamur.ai/free/en/papers/liu-2026-siniticmterror-machine-translation-dataset-error/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Hannah Liu, Junghyun Min, Annie En-Shiun Lee, et al.*2026* **[Why do Large Language Models Fail in Low-resource Translation? Unraveling the Token Dynamics of Large Language Models for Machine Translation](https://md.research.elamur.ai/free/en/papers/qian-2026-why-do-large-language-models/) [Annual Conference of the European Association for Machine Translation (EAMT)](https://md.research.elamur.ai/free/en/venues/eamt/)**Shenbin Qian, Yves Scherrer*2026*

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

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