---
title: "Who cites Aakanksha Naik — page 2 — 874 papers of our library"
description: "874 papers of the Elamur Research library cite the works of Aakanksha Naik, found in their bibliographies · page 2 of 3 · 101–200; the most…"
canonical: "https://research.elamur.ai/free/en/authors/aakanksha-naik/cited-by/page/2/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/aakanksha-naik/cited-by/page/2/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Aakanksha Naik](https://md.research.elamur.ai/free/en/authors/aakanksha-naik/) / [cited by](https://md.research.elamur.ai/free/en/authors/aakanksha-naik/cited-by/) / page 2

# Who cites Aakanksha Naik

1,001 citations of their works from 874 papers of our library (of the 149,439 whose bibliographies we read) · 232 with a page here, the most cited first · page 2 of 3 · 101–200 · [← Aakanksha Naik](https://md.research.elamur.ai/free/en/authors/aakanksha-naik/)

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

101. [**Language models scale reliably with over-training and on downstream tasks arXiv:2403.08540**Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/gadre-2024-language-models-scale-reliably-over/)
102. [**Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies arXiv:2407.13623**Chaofan Tao, Qian Liu, Longxu Dou, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/tao-2024-scaling-laws-vocabulary-larger-models/)
103. [**olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models arXiv:2502.18443**Jake Poznanski, Aman Rangapur, Jon Borchardt, et al.*2025 · 44 citations*](https://md.research.elamur.ai/free/en/papers/poznanski-2025-olmocr-unlocking-trillions-tokens-pdfs/)
104. **[Fishing for Magikarp: Automatically Detecting Under-trained Tokens in Large Language Models](https://md.research.elamur.ai/free/en/papers/land-2024-fishing-magikarp-automatically-detecting-under/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sander Land, Max Bartolo*2024 · 42 citations*
105. **[Paloma: A Benchmark for Evaluating Language Model Fit](https://md.research.elamur.ai/free/en/papers/magnusson-2023-paloma-benchmark-evaluating-language-model/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Ian Magnusson, Akshita Bhagia, Valentin Hofmann, et al.*2023 · 42 citations*
106. [**Auxiliary task demands mask the capabilities of smaller language models arXiv:2404.02418**Jennifer Hu, Michael C. Frank*2024 · 41 citations*](https://md.research.elamur.ai/free/en/papers/hu-2024-auxiliary-task-demands-mask-capabilities/)
107. **[FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions](https://md.research.elamur.ai/free/en/papers/weller-2025-followir-evaluating-teaching-information-retrieval/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Orion Weller, Benjamin Chang, Sean MacAvaney, et al.*2025 · 40 citations*
108. [**CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting arXiv:2404.10199**Huihan Li, Liwei Jiang, Jena D. Hwang, et al.*2024 · 40 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-culture-gen-revealing-global-cultural/)
109. [**SlimPajama-DC: Understanding Data Combinations for LLM Training arXiv:2309.10818**Zhiqiang Shen, Tianhua Tao, Liqun Ma, et al.*2023 · 38 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-slimpajama-dc-understanding-data-combinations/)
110. **[An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language Inference](https://md.research.elamur.ai/free/en/papers/liu-2020-empirical-study-model-agnostic-debiasing/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Tianyu Liu, Xin Zheng, Xiaoan Ding, et al.*2020 · 37 citations*
111. [**On the Diversity of Synthetic Data and its Impact on Training Large Language Models arXiv:2410.15226**Hao Chen, Abdul Waheed, Xiang Li, et al.*2024 · 35 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-diversity-synthetic-data-impact-training/)
112. **[Compositional Evaluation on Japanese Textual Entailment and Similarity](https://md.research.elamur.ai/free/en/papers/yanaka-2022-compositional-evaluation-japanese-textual-entailment/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Hitomi Yanaka, Koji Mineshima*2022 · 33 citations*
113. **[Benchmarking Linguistic Diversity of Large Language Models](https://md.research.elamur.ai/free/en/papers/guo-2025-benchmarking-linguistic-diversity-large-language/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Yanzhu Guo, Guokan Shang, Chloé Clavel*2025 · 32 citations*
114. **[Detection and Measurement of Syntactic Templates in Generated Text](https://md.research.elamur.ai/free/en/papers/shaib-2024-detection-measurement-syntactic-templates-generated/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chantal Shaib, Yanai Elazar, Junyi Jessy Li, et al.*2024 · 32 citations*
115. **[SEA-LION: Southeast Asian Languages in One Network](https://md.research.elamur.ai/free/en/papers/ng-2025-sea-lion-southeast-asian-languages/) [International Joint Conference on Natural Language Processing (IJCNLP)](https://md.research.elamur.ai/free/en/venues/ijcnlp/)**Raymond Ng, Thanh Ngan Nguyen, Huang Yuli, et al.*2025 · 29 citations*
116. **[Towards Effective and Efficient Continual Pre-training of Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-effective-efficient-continual-pre-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jie Chen, Zhipeng Chen, Jiapeng Wang, et al.*2025 · 29 citations*
117. [**NovelQA: Benchmarking Question Answering on Documents Exceeding 200K Tokens arXiv:2403.12766**Cunxiang Wang, Ruoxi Ning, Boqi Pan, et al.*2024 · 29 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-novelqa-benchmarking-question-answering-documents/)
118. [**Baichuan-Omni-1.5 Technical Report arXiv:2501.15368**Yadong Li, Jun Liu, Tao Zhang, et al.*2025 · 28 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-baichuan-omni-1-5/)
119. [**DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research arXiv:2511.19399**Rulin Shao, Akari Asai, Shannon Zejiang Shen, et al.*2025 · 28 citations*](https://md.research.elamur.ai/free/en/papers/shao-2025-dr-tulu-reinforcement-learning-evolving/)
120. **[Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above](https://md.research.elamur.ai/free/en/papers/balepur-2025-which-these-best-describes-multiple/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nishant Balepur, Rachel Rudinger, Jordan Boyd-Graber*2025 · 28 citations*
121. [**JetMoE: Reaching Llama2 Performance with 0.1M Dollars arXiv:2404.07413**Yikang Shen, Zhen Guo, Tianle Cai, et al.*2024 · 28 citations*](https://md.research.elamur.ai/free/en/papers/shen-2024-jetmoe-reaching-llama2-performance-0/)
122. **[HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/liu-2020-hyponli-exploring-artificial-patterns-hypothesis/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Tianyu Liu, Xin Zheng, Baobao Chang, et al.*2020 · 28 citations*
123. [**SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference**Martin Schmitt, Hinrich Schütze*2019 · 28 citations*](https://md.research.elamur.ai/free/en/papers/schmitt-2019-sherliic-typed-event-focused-lexical/)
124. **[Do Large Language Models have an English Accent? Evaluating and Improving the Naturalness of Multilingual LLMs](https://md.research.elamur.ai/free/en/papers/guo-2025-do-large-language-models-have/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yanzhu Guo, Simone Conia, Zelin Zhou, et al.*2025 · 25 citations*
125. **[Aligning LLMs with Individual Preferences via Interaction](https://md.research.elamur.ai/free/en/papers/wu-2025-aligning-llms-individual-preferences-interaction/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Shujin Wu, Yi R. Fung, Cheng Qian, et al.*2025 · 24 citations*
126. **[IberoBench: A Benchmark for LLM Evaluation in Iberian Languages](https://md.research.elamur.ai/free/en/papers/baucells-2025-iberobench-benchmark-llm-evaluation-iberian/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Irene Baucells, Javier Aula-Blasco, Iria de-Dios-Flores, et al.*2025 · 24 citations*
127. **[A Survey of Large Language Models for Arabic Language and its Dialects](https://md.research.elamur.ai/free/en/papers/mashaabi-2024-survey-large-language-models-arabic/) [ACM Transactions on Asian and Low-Resource Language Information Processing](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-asian-and-low-resource-language-information-processing/)**Malak Mashaabi, Shahad Al-Khalifa, Hend Al-Khalifa*2024 · 24 citations*
128. [**Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models arXiv:2005.14709**Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro*2020 · 24 citations*](https://md.research.elamur.ai/free/en/papers/schlegel-2020-leaderboards-survey-methods-revealing-weaknesses/)
129. **[An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT)](https://md.research.elamur.ai/free/en/papers/burchell-2025-expanded-massive-multilingual-dataset-high/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Laurie Burchell, Ona de Gibert, Nikolay Arefyev, et al.*2025 · 23 citations*
130. [**RWKV-7 "Goose" with Expressive Dynamic State Evolution arXiv:2503.14456**Bo Peng, Ruichong Zhang, Daniel Goldstein, et al.*2025 · 23 citations*](https://md.research.elamur.ai/free/en/papers/peng-2025-rwkv-7-goose-expressive-dynamic/)
131. **[HALoGEN: Fantastic LLM Hallucinations and Where to Find Them](https://md.research.elamur.ai/free/en/papers/ravichander-2025-halogen-fantastic-llm-hallucinations-where/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Abhilasha Ravichander, Shrusti Ghela, David Wadden, et al.*2025 · 22 citations*
132. **[A Survey on Evaluation of LLM-based Agents](https://md.research.elamur.ai/free/en/papers/yehudai-2026-survey-evaluation-llm-based-agents/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Asaf Yehudai, Lilach Eden, Alan Li, et al.*2026 · 21 citations*
133. **[AI4Research: A Survey of Artificial Intelligence for Scientific Research](https://md.research.elamur.ai/free/en/papers/chen-2025-ai4research-survey-artificial-intelligence/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Mingda Yang, Libo Qin, et al.*2025 · 21 citations*
134. [**NeoBERT: A Next-Generation BERT arXiv:2502.19587**Lola Le Breton, Quentin Fournier, Mariam El Mezouar, et al.*2025 · 20 citations*](https://md.research.elamur.ai/free/en/papers/breton-2025-neobert-next-generation-bert/)
135. [**Baichuan-Omni Technical Report arXiv:2410.08565**Yadong Li, Haoze Sun, Mingan Lin, et al.*2024 · 18 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-baichuan-omni-technical-report/)
136. **[Stress Test Evaluation of Transformer-based Models in Natural Language Understanding Tasks](https://md.research.elamur.ai/free/en/papers/aspillaga-2020-stress-test-evaluation-transformer-based/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Carlos Aspillaga, Andrés Carvallo, Vladimir Araujo*2020 · 17 citations*
137. **[Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum](https://md.research.elamur.ai/free/en/papers/pouransari-2024-dataset-decomposition-faster-llm-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hadi Pouransari, Chun-Liang Li, Jen-Hao Rick Chang, et al.*2024 · 16 citations*
138. [**ChemAgent: Self-updating Library in Large Language Models Improves Chemical Reasoning arXiv:2501.06590**Xiangru Tang, Tianyu Hu, Muyang Ye, et al.*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/tang-2025-chemagent-self-updating-library-large/)
139. **[AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters](https://md.research.elamur.ai/free/en/papers/lucy-2024-aboutme-self-descriptions-webpages-document/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Li Lucy, Suchin Gururangan, Luca Soldaini, et al.*2024 · 15 citations*
140. [**Adversarial Analysis of Natural Language Inference Systems arXiv:1912.03441**Tiffany Chien, Jugal Kalita*2019 · 15 citations*](https://md.research.elamur.ai/free/en/papers/chien-2019-adversarial-analysis-natural-language-inference/)
141. **[Curing the SICK and Other NLI Maladies](https://md.research.elamur.ai/free/en/papers/kalouli-2023-curing-sick-other-nli-maladies/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Aikaterini-Lida Kalouli, Hai Hu, Alexander F. Webb, et al.*2023 · 14 citations*
142. **[Looking at the Overlooked: An Analysis on the Word-Overlap Bias in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/rajaee-2022-looking-overlooked-analysis-word-overlap/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sara Rajaee, Yadollah Yaghoobzadeh, Mohammad Taher Pilehvar*2022 · 13 citations*
143. **[Dolomites: Domain-Specific Long-Form Methodical Tasks](https://md.research.elamur.ai/free/en/papers/malaviya-2025-dolomites-domain-specific-long-form/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Chaitanya Malaviya, Priyanka Agrawal, Kuzman Ganchev, et al.*2025 · 12 citations*
144. **[Benchmarking LLaMA-3 on Arabic Language Generation Tasks](https://md.research.elamur.ai/free/en/papers/khondaker-2024-benchmarking-llama-3-arabic-language/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Md Tawkat Islam Khondaker, Numaan Naeem, Fatimah Khan, et al.*2024 · 12 citations*
145. [**Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase Pretraining arXiv:2412.15285**Steven Feng, Shrimai Prabhumoye, Kezhi Kong, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/feng-2024-maximize-data-s-potential-enhancing/)
146. [**MiniPLM: Knowledge Distillation for Pre-Training Language Models arXiv:2410.17215**Yuxian Gu, Hao Zhou, Fandong Meng, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/gu-2024-miniplm-knowledge-distillation-pre-training/)
147. **[How Can We Effectively Expand the Vocabulary of LLMs with 0.01GB of Target Language Text?](https://md.research.elamur.ai/free/en/papers/yamaguchi-2026-how-can-we-effectively-expand/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Atsuki Yamaguchi, Aline Villavicencio, Nikolaos Aletras*2026 · 11 citations*
148. **[Does External Knowledge Help Explainable Natural Language Inference? Automatic Evaluation vs. Human Ratings](https://md.research.elamur.ai/free/en/papers/schuff-2021-does-external-knowledge-help-explainable/) [BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP](https://md.research.elamur.ai/free/en/venues/blackboxnlp/)**Hendrik Schuff, Hsiu-Yu Yang, Heike Adel, et al.*2021 · 11 citations*
149. **[Multiple LLM Agents Debate for Equitable Cultural Alignment](https://md.research.elamur.ai/free/en/papers/ki-2025-multiple-llm-agents-debate-equitable/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dayeon Ki, Rachel Rudinger, Tianyi Zhou, et al.*2025 · 10 citations*
150. **[How Often Are Errors in Natural Language Reasoning Due to Paraphrastic Variability?](https://md.research.elamur.ai/free/en/papers/srikanth-2024-how-often-are-errors-natural/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Neha Srikanth, Marine Carpuat, Rachel Rudinger*2024 · 10 citations*
151. **[How Fast can BERT Learn Simple Natural Language Inference?](https://md.research.elamur.ai/free/en/papers/lin-2021-how-fast-can-bert-learn/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Yi-Chung Lin, Keh-Yih Su*2021 · 10 citations*
152. **[Structured Moral Reasoning in Language Models: A Value-Grounded Evaluation Framework](https://md.research.elamur.ai/free/en/papers/chakraborty-2025-structured-moral-reasoning-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mohna Chakraborty, Lu Wang, David Jurgens*2025 · 9 citations*
153. **[TESS 2: A Large-Scale Generalist Diffusion Language Model](https://md.research.elamur.ai/free/en/papers/tae-2025-tess-2-large-scale-generalist/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jaesung Tae, Hamish Ivison, Sachin Kumar, et al.*2025 · 9 citations*
154. **[CER: Confidence Enhanced Reasoning in LLMs](https://md.research.elamur.ai/free/en/papers/razghandi-2025-cer-confidence-enhanced-reasoning-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ali Razghandi, Seyed Mohammad Hadi Hosseini, Mahdieh Soleymani Baghshah*2025 · 8 citations*
155. **[Nudging: Inference-time Alignment of LLMs via Guided Decoding](https://md.research.elamur.ai/free/en/papers/fei-2025-nudging-inference-time-alignment-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yu Fei, Yasaman Razeghi, Sameer Singh*2025 · 8 citations*
156. **[Improving Language Models Trained on Translated Data with Continual Pre-Training and Dictionary Learning Analysis](https://md.research.elamur.ai/free/en/papers/boughorbel-2024-improving-language-models-trained-translated/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Sabri Boughorbel, Md Rizwan Parvez, Majd Hawasly*2024 · 8 citations*
157. [**Scaling Smart: Accelerating Large Language Model Pre-training with Small Model Initialization arXiv:2409.12903**Mohammad Samragh, Iman Mirzadeh, Keivan Alizadeh Vahid, et al.*2024 · 8 citations*](https://md.research.elamur.ai/free/en/papers/samragh-2024-scaling-smart-accelerating-large-language/)
158. **[Simple but Challenging: Natural Language Inference Models Fail on Simple Sentences](https://md.research.elamur.ai/free/en/papers/luo-2022-simple-but-challenging-natural-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Cheng Luo, Wei Liu, Jieyu Lin, et al.*2022 · 7 citations*
159. **[Minimal Pair-Based Evaluation of Code-Switching](https://md.research.elamur.ai/free/en/papers/sterner-2025-minimal-pair-based-evaluation-code/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Igor Sterner, Simone Teufel*2025 · 6 citations*
160. **[Sparse Logit Sampling: Accelerating Knowledge Distillation in LLMs](https://md.research.elamur.ai/free/en/papers/anshumann-2025-sparse-logit-sampling-accelerating-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Anshumann, Mohd Abbas Zaidi, Akhil Kedia, et al.*2025 · 6 citations*
161. **[Detecting Training Data of Large Language Models via Expectation Maximization](https://md.research.elamur.ai/free/en/papers/kim-2026-detecting-training-data-large-language/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Gyuwan Kim, Yang Li, Evangelia Spiliopoulou, et al.*2026 · 5 citations*
162. **[WikiVideo: Article Generation from Multiple Videos](https://md.research.elamur.ai/free/en/papers/martin-2026-wikivideo-article-generation-multiple-videos/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Alexander Martin, Reno Kriz, William Gantt Walden, et al.*2026 · 5 citations*
163. **[Are BabyLMs Deaf to Gricean Maxims? A Pragmatic Evaluation of Sample-efficient Language Models](https://md.research.elamur.ai/free/en/papers/askari-2025-are-babylms-deaf-gricean-maxims/) [BabyLM Workshop](https://md.research.elamur.ai/free/en/venues/babylm/)**Raha Askari, Sina Zarrieß, Özge Alacam, et al.*2025 · 4 citations*
164. **[Evaluating Numeracy of Language Models as a Natural Language Inference Task](https://md.research.elamur.ai/free/en/papers/mahendra-2025-evaluating-numeracy-language-models-as/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Rahmad Mahendra, Damiano Spina, Lawrence Cavedon, et al.*2025 · 4 citations*
165. **[Mechanisms vs. Outcomes: Probing for Syntax Fails to Explain Performance on Targeted Syntactic Evaluations](https://md.research.elamur.ai/free/en/papers/agarwal-2025-mechanisms-vs-outcomes-probing-syntax/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ananth Agarwal, Jasper Jian, Christopher D Manning, et al.*2025 · 4 citations*
166. **[It is not True that Transformers are Inductive Learners: Probing NLI Models with External Negation](https://md.research.elamur.ai/free/en/papers/sullivan-2024-it-is-not-true-that/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Michael Sullivan*2024 · 4 citations*
167. **[FANCY: A Diagnostic Data-Set for NLI Models](https://md.research.elamur.ai/free/en/papers/rocchietti-2021-fancy-diagnostic-data-set-nli/) [Italian Conference on Computational Linguistics](https://md.research.elamur.ai/free/en/venues/clicit/)**Guido Rocchietti, Flavia Achena, Giuseppe Marziano, et al.*2021 · 4 citations*
168. **[Adapting Psycholinguistic Research for LLMs: Gender-inclusive Language in a Coreference Context](https://md.research.elamur.ai/free/en/papers/bartl-2025-adapting-psycholinguistic-research-llms-gender/) [Workshop on Gender Bias in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/gebnlp/)**Marion Bartl, Thomas Brendan Murphy, Susan Leavy*2025 · 3 citations*
169. **[How Hard is this Test Set? NLI Characterization by Exploiting Training Dynamics](https://md.research.elamur.ai/free/en/papers/cosma-2024-how-hard-is-this-test/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Adrian Cosma, Stefan Ruseti, Mihai Dascalu, et al.*2024 · 3 citations*
170. [**A Sovereign, Open-Source Foundation Model for German and English arXiv:2607.09424**Soofi-Team: Benedikt Droste, David Fitzek, Ruben Harle, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/droste-2026-sovereign-open-source-foundation-model/)
171. [**Dicta-LM 3.0: Advancing The Frontier of Hebrew Sovereign LLMs**Shaltiel Shmidman, Avi Shmidman, Amir DN Cohen, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/shmidman-2026-dicta-lm-3-0-advancing/)
172. **[HTMuon: Improving Muon via Heavy-Tailed Spectral Correction](https://md.research.elamur.ai/free/en/papers/pang-2026-htmuon-improving-muon-heavy-tailed/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tianyu Pang, Yujie Fang, Zihang Liu, et al.*2026 · 2 citations*
173. **[Improving Romanian LLM Pretraining Data using Diversity and Quality Filtering](https://md.research.elamur.ai/free/en/papers/negoita-2026-improving-romanian-llm-pretraining-data/) [Workshop on Language Models for Low-Resource Languages](https://md.research.elamur.ai/free/en/venues/loreslm/)**Vlad-Andrei Negoiță, Mihai Masala, Traian Rebedea*2026 · 2 citations*
174. **[Kenji-Endo: a BabyLM @EVALITA](https://md.research.elamur.ai/free/en/papers/scozzaro-2026-kenji-endo-babylm-evalita/) [Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop](https://md.research.elamur.ai/free/en/venues/evalita/)**Calogero Jerik Scozzaro, Matteo Rinaldi, Gianluca Mittone, et al.*2026 · 2 citations*
175. **[QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/min-2026-quco-rag-quantifying-uncertainty-pre/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Dehai Min, Kailin Zhang, Tongtong Wu, et al.*2026 · 2 citations*
176. **[Thunder-NUBench: A Benchmark for LLMs' Sentence-Level Negation Understanding](https://md.research.elamur.ai/free/en/papers/so-2026-thunder-nubench-benchmark-llms-sentence/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yeonkyoung So, Gyuseong Lee, Sungmok Jung, et al.*2026 · 2 citations*
177. **[Automatic Extraction of Clausal Embedding Based on Large-Scale English Text Data](https://md.research.elamur.ai/free/en/papers/carslaw-2025-automatic-extraction-clausal-embedding-based/) [Society for Computation in Linguistics (SCiL)](https://md.research.elamur.ai/free/en/venues/scil/)**Iona Carslaw, Sivan Milton, Nicolas Navarre, et al.*2025 · 2 citations*
178. [**The German Commons - 154 Billion Tokens of Openly Licensed Text for German Language Models arXiv:2510.13996**Lukas Gienapp, Christopher Schroder, Stefan Schweter, et al.*2025 · 2 citations*](https://md.research.elamur.ai/free/en/papers/gienapp-2025-german-commons-154-billion-tokens/)
179. [**Hypothesis-only Biases in Large Language Model-Elicited Natural Language Inference arXiv:2410.08996**Grace Proebsting, Adam Poliak*2024 · 2 citations*](https://md.research.elamur.ai/free/en/papers/proebsting-2024-hypothesis-only-biases-large-language/)
180. **[Pragmatic Reasoning Unlocks Quantifier Semantics for Foundation Models](https://md.research.elamur.ai/free/en/papers/li-2023-pragmatic-reasoning-unlocks-quantifier-semantics/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yiyuan Li, Rakesh Menon, Sayan Ghosh, et al.*2023 · 2 citations*
181. [**Synthetic Dataset for Evaluating Complex Compositional Knowledge for Natural Language Inference Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE)**Sushma Anand Akoju, Robert Vacareanu, Eduardo Blanco, et al.*2023 · 2 citations*](https://md.research.elamur.ai/free/en/papers/akoju-2023-synthetic-dataset-evaluating-complex-compositional/)
182. **[Addressing the Ecological Fallacy in Larger LMs with Human Context](https://md.research.elamur.ai/free/en/papers/soni-2026-addressing-ecological-fallacy-larger-lms/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Nikita Soni, Dhruv Vijay Kunjadiya, Pratham Piyush Shah, et al.*2026 · 1 citation*
183. **[Aligned Probing: Relating Toxic Behavior and Model Internals](https://md.research.elamur.ai/free/en/papers/waldis-2026-aligned-probing-relating-toxic-behavior/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Andreas Waldis, Vagrant Gautam, Anne Lauscher, et al.*2026 · 1 citation*
184. **[From Articles to Premises: Building PrimeFacts, an Extraction Methodology and Resource for Fact-Checking Evidence](https://md.research.elamur.ai/free/en/papers/sahitaj-2026-articles-premises-building-primefacts-extraction/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Premtim Sahitaj, Jawan Kolanowski, Ariana Sahitaj, et al.*2026 · 1 citation*
185. [**KletterMix: Climbing Toward High-Quality German Pretraining Data - The Full Report arXiv:2606.03773**Maurice Kraus, Ruben Harle, Sebastian Sztwiertnia, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/kraus-2026-klettermix-climbing-toward-high-quality/)
186. **[NeoAraBERT: A Modern Foundation Model for Arabic Embeddings with Diacritics-Aware Tokenization and POS-Targeted Masking](https://md.research.elamur.ai/free/en/papers/chakra-2026-neoarabert-modern-foundation-model-arabic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chadi Abou Chakra, Hadi Khaled Hamoud, Osama Rakan Al Mraikhat, et al.*2026 · 1 citation*
187. **[Personal Information Parroting in Language Models](https://md.research.elamur.ai/free/en/papers/subramani-2026-personal-information-parroting-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nishant Subramani, Kshitish Ghate, Mona T. Diab*2026 · 1 citation*
188. **[Start Making Sense(s): A Developmental Probe of Attention Specialization Using Lexical Ambiguity](https://md.research.elamur.ai/free/en/papers/riviere-2026-start-making-sense-s-developmental/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Pamela D. Rivière, Sean Trott*2026 · 1 citation*
189. **[Towards a Diagnostic and Predictive Evaluation Methodology for Sequence Labeling Tasks](https://md.research.elamur.ai/free/en/papers/mellado-2026-diagnostic-predictive-evaluation-methodology-sequence/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Elena Alvarez-Mellado, Julio Gonzalo*2026 · 1 citation*
190. **[ArabicWeb-Edu: Educational Quality Data for Arabic LLM Training](https://md.research.elamur.ai/free/en/papers/hawasly-2025-arabicweb-edu-educational-quality-data/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Majd Hawasly, Tasnim Mohiuddin, Hamdy Mubarak, et al.*2025 · 1 citation*
191. **[Biases in Large Language Model-Elicited Text: A Case Study in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/proebsting-2025-biases-large-language-model-elicited/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Grace Proebsting, Adam Poliak*2025 · 1 citation*
192. [**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) Acta Automatica Sinica (自动化学报)**Anda Tang (唐岸达), Zhouchen Lin (林宙辰)*2026*](https://md.research.elamur.ai/free/en/papers/tang-2026-parameter-efficient-fine/)
193. **[Beyond Static Synthetic Noise: Assessing the Robustness of Large Language Models to Natural Context Variation in the Real World](https://md.research.elamur.ai/free/en/papers/wu-2026-beyond-static-synthetic-noise-assessing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yulong Wu, Viktor Schlegel, Riza Batista-Navarro*2026*
194. **[CheckMIABench: Firm Foundations For Membership Inference Attacks on Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-checkmiabench-firm-foundations-membership-inference/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jeffrey George Wang, Jason Wang, Marvin Li, et al.*2026*
195. **[Continuous Interpretive Steering for Scalar Diversity](https://md.research.elamur.ai/free/en/papers/cho-2026-continuous-interpretive-steering-scalar-diversity/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ye-eun Cho*2026*
196. **[Datasets for Scientific Literature Understanding: A Survey](https://md.research.elamur.ai/free/en/papers/zhang-2026-datasets-scientific-literature-understanding-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuanzhe Zhang, Xun Zhao, Maodi Hu, et al.*2026*
197. **[Detecting Non-Membership in LLM Training Data via Rank Correlations](https://md.research.elamur.ai/free/en/papers/shetty-2026-detecting-non-membership-llm-training/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Pranav Shetty, Mirazul Haque, Zhiqiang Ma, et al.*2026*
198. **[DynamixSFT: Dynamic Mixture Optimization of Instruction Tuning Collections](https://md.research.elamur.ai/free/en/papers/shin-2026-dynamixsft-dynamic-mixture-optimization-instruction/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haebin Shin, Lei Ji, Xiao Liu, et al.*2026*
199. [**E-CONAN (Entailment, CONtradition And Neutral) Diagnostics Dataset Investigating Linguistic Phenomena in Arabic Natural Language Understanding arXiv:2609.33530**Khloud AL Jallad, Nada Ghneim, Ghaida Rebdawi*2026*](https://md.research.elamur.ai/free/en/papers/jallad-2026-e-conan-entailment-contradition-neutral/)
200. **[EfficientLLM: Unified Pruning-Aware Pretraining for Auto-Designed Compact Language Models](https://md.research.elamur.ai/free/en/papers/xing-2026-efficientllm-unified-pruning-aware-pretraining/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xingrun Xing, Zheng Liu, Shitao Xiao, et al.*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/authors/aakanksha-naik/) — Aakanksha Naik