---
title: "Who cites Vaishaal Shankar — page 2 — 546 citing papers"
description: "546 papers of the Elamur Research library cite the works of Vaishaal Shankar, found in their bibliographies · page 2 of 2 · 101–125; the most…"
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---
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# Who cites Vaishaal Shankar

618 citations of their works from 546 papers of our library (of the 149,439 whose bibliographies we read) · 125 with a page here, the most cited first · page 2 of 2 · 101–125 · [← Vaishaal Shankar](https://md.research.elamur.ai/free/en/authors/vaishaal-shankar/)

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

101. **[Where Reasoning Breaks: Logic-Aware Path Selection by Controlling Logical Connectives in LLMs Reasoning Chains](https://md.research.elamur.ai/free/en/papers/park-2026-where-reasoning-breaks-logic-aware/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Seunghyun Park, Yuanyuan Lei*2026 · 1 citation*
102. **[A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAMΔ Integration into Upcycled MoE](https://md.research.elamur.ai/free/en/papers/zhou-2026-data-efficient-path-multilingual-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hao Zhou, Tianhao Li, Zhijun Wang, et al.*2026*
103. **[Biomed-Enriched: Data-Efficient Biomedical Pretraining via Paragraph-Level Annotation](https://md.research.elamur.ai/free/en/papers/touchent-2026-biomed-enriched-data-efficient-biomedical/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Rian Touchent, Nathan Godey, Éric Villemonte de la Clergerie*2026*
104. **[Challenging the Myth: A Research Arc on LLMs as Human Simulacra](https://md.research.elamur.ai/free/en/papers/munker-2026-challenging-myth-research-arc-llms/) [Big Picture v2: Crafting a Research Narrative](https://md.research.elamur.ai/free/en/venues/bigpicture/)**Simon Münker, Achim Rettinger, Damian Trilling*2026*
105. **[Dialectal Filtering: Synthesizing Kurdish Corpora for Low-Resource Varieties by Utilizing “Noise” in Large Textual Data](https://md.research.elamur.ai/free/en/papers/schuler-2026-dialectal-filtering-synthesizing-kurdish-corpora/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Christian Schuler, Raman Ahmad, Ānrán Wáng, et al.*2026*
106. **[DOSE: Data Selection for Multi-Modal LLMs via Off-the-Shelf Models](https://md.research.elamur.ai/free/en/papers/wu-2026-dose-data-selection-multi-modal/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Biao Wu, Yiwu Zhong, Meng Fang, et al.*2026*
107. **[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*
108. **[How Can Synthetic Data Improve Multilingual Language Model Pretraining? A Data Quality Perspective](https://md.research.elamur.ai/free/en/papers/zhu-2026-how-can-synthetic-data-improve/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tongyao Zhu, Qian Liu, Chang Ma, et al.*2026*
109. **[LEDOM: Reverse Language Model](https://md.research.elamur.ai/free/en/papers/yin-2026-ledom-reverse-language-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xunjian Yin, Sitao Cheng, Yuxi Xie, et al.*2026*
110. **[LoopCoder: Scaling Code Intelligence via Looped Language Models](https://md.research.elamur.ai/free/en/papers/yang-2026-loopcoder-scaling-code-intelligence-looped/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jian Yang, Wei Zhang, Shuyue Guo, et al.*2026*
111. **[Multilingual-To-Multimodal (M2M): Unlocking New Languages with Monolingual Text](https://md.research.elamur.ai/free/en/papers/pasi-2026-multilingual-multimodal-m2m-unlocking-new/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Piyush Singh Pasi*2026*
112. [**Probing the Dynamics of Syntactic Ability Acquisition Throughout LLM Pretraining**Hiroshi Matsuda, Masayuki Asahara*2026*](https://md.research.elamur.ai/free/en/papers/matsuda-2026-probing-dynamics-syntactic-ability-acquisition/)
113. **[Pru-CoT: Towards Efficient Reasoning Distillation via Pruning Chain-of-Thought](https://md.research.elamur.ai/free/en/papers/liu-2026-pru-cot-efficient-reasoning-distillation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Han Liu, Shuotian Ma, Hui Li, et al.*2026*
114. **[ReasMark: A Robust Watermark for Attributing LLM Reasoning Under Knowledge Distillation Attacks](https://md.research.elamur.ai/free/en/papers/lv-2026-reasmark-robust-watermark-attributing-llm/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peizhuo Lv, Ruihua Zhou, Yunpeng Li, et al.*2026*
115. **[Rethinking Data Mixing from the Perspective of Large Language Models](https://md.research.elamur.ai/free/en/papers/xu-2026-rethinking-data-mixing-perspective-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuanjian Xu, Tianze Sun, Changwei Xu, et al.*2026*
116. **[Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives](https://md.research.elamur.ai/free/en/papers/liu-2026-rethinking-data-mixture-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yajiao Liu, Congliang Chen, Junchi Yang, et al.*2026*
117. **[Rewriting History: A Recipe for Interventional Analyses to Study Data Effects on Model Behavior](https://md.research.elamur.ai/free/en/papers/nadkarni-2026-rewriting-history-recipe-interventional-analyses/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Rahul Nadkarni, Yanai Elazar, Hila Gonen, et al.*2026*
118. **[Scaling Law for Multimodal Large Language Model Supervised Fine-Tuning](https://md.research.elamur.ai/free/en/papers/zhang-2026-scaling-law-multimodal-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**YiFan Zhang, Tao Yu, Feng Li, et al.*2026*
119. **[SciPedia: Unlocking the Value of Scientific Data for Pre-training](https://md.research.elamur.ai/free/en/papers/qin-2026-scipedia-unlocking-value-scientific-data/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yiwei Qin, Zhen Huang, Tiantian Mi, et al.*2026*
120. **[SELECting over Tokens: Curating Pre-training Data at Scale via Token Classification](https://md.research.elamur.ai/free/en/papers/tong-2026-selecting-over-tokens-curating-pre/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xin Tong, Weidong Zhang, Jiaang Li, et al.*2026*
121. **[Systematic Evaluation of the Quality of Synthetic Clinical Notes Rephrased by LLMs at Million-Note Scale](https://md.research.elamur.ai/free/en/papers/liu-2026-systematic-evaluation-quality-synthetic-clinical/) [BioNLP](https://md.research.elamur.ai/free/en/venues/bionlp/)**Jinghui Liu, Sarvesh Soni, Anthony Nguyen*2026*
122. **[Tailored Primitive Initialization is the Secret Key to Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/yao-2026-tailored-primitive-initialization-is-secret/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yihang Yao, Guangtao Zeng, Raina Wu, et al.*2026*
123. **[TARo: Token-level Adaptive Routing for LLM Test-time Alignment](https://md.research.elamur.ai/free/en/papers/rai-2026-taro-token-level-adaptive-routing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Arushi Rai, Qiang Zhang, Hanqing Zeng, et al.*2026*
124. **[UniGeM: Unifying Data Selection and Mixing via Geometric Exploration and Mining](https://md.research.elamur.ai/free/en/papers/wang-2026-unigem-unifying-data-selection-mixing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Changhao Wang, Yunfeiyu, Xinhao Yao, et al.*2026*
125. [**SmolKalam: Ensemble Quality-Filtered Translation at Scale for High Quality Arabic Post-Training Data arXiv:2511.18411**Sultan Alrashed, Chadi Helwe, Francesco Orabona*2025*](https://md.research.elamur.ai/free/en/papers/alrashed-2025-smolkalam-ensemble-quality-filtered-translation/)

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