---
title: "Who cites Rulin Shao — page 2 — 552 papers of our library"
description: "552 papers of the Elamur Research library cite the works of Rulin Shao, found in their bibliographies · page 2 of 2 · 101–147; the most cited first."
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# Who cites Rulin Shao

590 citations of their works from 552 papers of our library (of the 149,439 whose bibliographies we read) · 147 with a page here, the most cited first · page 2 of 2 · 101–147 · [← Rulin Shao](https://md.research.elamur.ai/free/en/authors/rulin-shao/)

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

101. **[Who Endorsed It? Measuring Authority Bias Across Expertise Levels in Language Models](https://md.research.elamur.ai/free/en/papers/mammen-2026-who-endorsed-it-measuring-authority/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Priyanka Mary Mammen, Emil Joswin, Shankar Venkitachalam*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. **[A Survey of Reasoning-Intensive Retrieval: Progress and Challenges](https://md.research.elamur.ai/free/en/papers/wei-2026-survey-reasoning-intensive-retrieval-progress/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yiyang Wei, Tingyu Song, Siyue Zhang, et al.*2026*
104. **[A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions](https://md.research.elamur.ai/free/en/papers/yu-2026-survey-reinforcement-learning-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiyin Yu, Yuchen Mou, Juncheng Yan, et al.*2026*
105. **[AfriqueLLM: How Data Mixing and Model Architecture Impact Continued Pre-training for African Languages](https://md.research.elamur.ai/free/en/papers/yu-2026-afriquellm-how-data-mixing-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hao Yu, Tianyi Xu, Michael A. Hedderich, et al.*2026*
106. **[AlgBench: To What Extent Do Large Reasoning Models Understand Algorithms?](https://md.research.elamur.ai/free/en/papers/sun-2026-algbench-what-extent-do-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Henan Sun, Kaichi Yu, Yuyao Wang, et al.*2026*
107. **[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*
108. **[Boosting Self-Consistency with Ranking](https://md.research.elamur.ai/free/en/papers/marina-2026-boosting-self-consistency-ranking/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Maria Marina, Daniil Moskovskiy, Sergey Pletenev, et al.*2026*
109. **[CentaurTA: A Self-Improving Human-Agents Collaboration Framework for Thematic Analysis](https://md.research.elamur.ai/free/en/papers/wang-2026-centaurta-self-improving-human-agents/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Lei Wang, Min Huang, Eduard Dragut*2026*
110. **[Chaining the Evidence: Robust Reinforcement Learning for Deep Search Agents with Citation-Aware Rubric Rewards](https://md.research.elamur.ai/free/en/papers/zhang-2026-chaining-evidence-robust-reinforcement-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiajie Zhang, Xin Lv, Ling Feng, et al.*2026*
111. **[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*
112. **[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*
113. **[Distributional Clarity: The Hidden Driver of RL-Friendliness in Large Language Models](https://md.research.elamur.ai/free/en/papers/sun-2026-distributional-clarity-hidden-driver-rl/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shaoning Sun, Mingzhu Cai, Huang He, et al.*2026*
114. [**Don't ‘Well, Actually' Me Unless You Know What You're Talking About: Weak Presupposition Verification Degrades General QA Performance arXiv:2608.06539**Shenran Wang, Vered Shwartz, Hila Gonen*2026*](https://md.research.elamur.ai/free/en/papers/wang-2026-don-t-well-actually-me/)
115. **[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*
116. **[Evaluating Generative Large Language Models for Portuguese Scientific Information Extraction](https://md.research.elamur.ai/free/en/papers/pinto-2026-evaluating-generative-large-language-models/) [Natural Scientific Language Processing (NSLP) @ LREC](https://md.research.elamur.ai/free/en/venues/nslp/)**Tomás Pinto, Catarina Silva, Hugo Goncalo Oliveira*2026*
117. **[Exploring Fine-Tuning for In-Context Retrieval and Efficient KV-Caching in Long-Context Language Models](https://md.research.elamur.ai/free/en/papers/molfese-2026-exploring-fine-tuning-context-retrieval/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Francesco Maria Molfese, Momchil Hardalov, Rexhina Blloshmi, et al.*2026*
118. **[Field of Science and Technology Classification of Academic Documents in Portuguese](https://md.research.elamur.ai/free/en/papers/simoes-2026-field-science-technology-classification-academic/) [International Conference on Computational Processing of Portuguese](https://md.research.elamur.ai/free/en/venues/propor/)**Ivo Simões, Hugo Gonçalo Oliveira, João Correia*2026*
119. **[From Narrow Unlearning to Emergent Misalignment in LLMs](https://md.research.elamur.ai/free/en/papers/mushtaq-2026-narrow-unlearning-emergent-misalignment-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Erum Mushtaq, Anil Ramakrishna, Satyapriya Krishna, et al.*2026*
120. **[Heterogeneity in Formal Linguistic Competence of Language Models: Is Data the Real Bottleneck?](https://md.research.elamur.ai/free/en/papers/renduchintala-2026-heterogeneity-formal-linguistic-competence-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**H S V N S Kowndinya Renduchintala, Sumit Bhatia*2026*
121. **[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*
122. **[How Retrieved Context Shapes Internal Representations in RAG](https://md.research.elamur.ai/free/en/papers/yeh-2026-how-retrieved-context-shapes-internal/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Samuel Yeh, Sharon Li*2026*
123. **[KisMATH: Do LLMs Have Knowledge of Implicit Structures in Mathematical Reasoning?](https://md.research.elamur.ai/free/en/papers/saha-2026-kismath-do-llms-have-knowledge/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Soumadeep Saha, Akshay Chaturvedi, Saptarshi Saha, et al.*2026*
124. **[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*
125. **[Let Retrievers Think Before Action: Thought-Augmented Embedding for Dense Retrieval](https://md.research.elamur.ai/free/en/papers/yan-2026-let-retrievers-think-before-action/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ruiran Yan, Wen Xiong, Ze Liu, et al.*2026*
126. **[MUC-4 Revisited: Document-level Event Analysis beyond Span-based Arguments](https://md.research.elamur.ai/free/en/papers/olsen-2026-muc-4-revisited-document-level/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Helene Bøsei Olsen, Erik Velldal, Lilja Øvrelid*2026*
127. **[Observations and Remedies for Large Language Model Bias in Self-Consuming Performative Loop](https://md.research.elamur.ai/free/en/papers/wang-2026-observations-remedies-large-language-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yaxuan Wang, Zhongteng Cai, Yujia Bao, et al.*2026*
128. **[PaperScope: A Multi-Modal Multi-Document Benchmark for Agentic Deep Research Across Massive Scientific Papers](https://md.research.elamur.ai/free/en/papers/xiong-2026-paperscope-multi-modal-multi-document/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Lei Xiong, Huaying Yuan, Zheng Liu, et al.*2026*
129. [**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/)
130. **[ProductResearch: Training E-Commerce Deep Research Agents via Multi-Agent Synthetic Trajectory Distillation](https://md.research.elamur.ai/free/en/papers/wang-2026-productresearch-training-e-commerce-deep/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiangyuan Wang, Kejun Xiao, Huaipeng Zhao, et al.*2026*
131. **[R ESEARCH QA: Evaluating Scholarly Question Answering at Scale Across 75 Fields with Survey-Mined Questions and Rubrics](https://md.research.elamur.ai/free/en/papers/yifei-2026-r-esearch-qa-evaluating-scholarly/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Li S. Yifei, Allen Chang, Chaitanya Malaviya, et al.*2026*
132. **[RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentation](https://md.research.elamur.ai/free/en/papers/xu-2026-rag-wild-effectiveness-llms-mixture/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ran Xu, Yuchen Zhuang, Yue Yu, et al.*2026*
133. **[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*
134. **[Rethinking Reasoning-Intensive Retrieval: Evaluating and Advancing Retrievers in Agentic Search Systems](https://md.research.elamur.ai/free/en/papers/zhao-2026-rethinking-reasoning-intensive-retrieval-evaluating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yilun Zhao, Jinbiao Wei, Tingyu Song, et al.*2026*
135. **[Rethinking the Idiomaticity Decomposability Hypothesis: Evidence from Distributional Learning](https://md.research.elamur.ai/free/en/papers/mi-2026-rethinking-idiomaticity-decomposability-hypothesis-evidence/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Maggie Mi, Golzar Atefi, Atsuki Yamaguchi, et al.*2026*
136. **[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*
137. **[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*
138. **[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*
139. **[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*
140. **[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*
141. **[Think Like You Execute: Verifiable Chain of Thought from Program Traces](https://md.research.elamur.ai/free/en/papers/thakur-2026-think-like-you-execute-verifiable/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shailja Thakur, Vaibhav Saxena, Rohan Kulkarni, et al.*2026*
142. **[Too Nice to Tell the Truth: Quantifying Agreeableness-Driven Sycophancy in Role-Playing Language Models](https://md.research.elamur.ai/free/en/papers/shah-2026-too-nice-tell-truth-quantifying/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Arya Shah, Deepali Mishra, Chaklam Silpasuwanchai*2026*
143. **[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*
144. **[Weight Tying Biases Token Embeddings Towards the Output Space](https://md.research.elamur.ai/free/en/papers/lopardo-2026-weight-tying-biases-token-embeddings/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Antonio Lopardo, Avyukth Harish, Catherine Arnett, et al.*2026*
145. **[Who Speaks for Whom? LLM-Generated Survey Data as a Proxy for Public Opinion](https://md.research.elamur.ai/free/en/papers/venkatakrishnan-2026-who-speaks-whom-llm-generated/) [Workshop on Natural Language Processing and Computational Social Science](https://md.research.elamur.ai/free/en/venues/nlpcss/)**Radhakrishnan Venkatakrishnan, Travis Brodbeck, Michael D. Young*2026*
146. **[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*
147. **[Survey on Key Technologies for Large Language Model Pre-training Systems (大语言模型预训练系统关键技术综述)](https://md.research.elamur.ai/free/en/papers/gao-2025-key-technologies-large/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Yanjie Gao (高彦杰), Yueguo Chen (陈跃国)*2025*

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