---
title: "Who cites Ruiyi Zhang — page 2 — 649 papers of our library"
description: "649 papers of the Elamur Research library cite the works of Ruiyi Zhang, found in their bibliographies · page 2 of 2 · 101–147; the most cited first."
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---
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# Who cites Ruiyi Zhang

652 citations of their works from 649 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 · [← Ruiyi Zhang](https://md.research.elamur.ai/free/en/authors/ruiyi-zhang/)

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

101. **[ReAttn: Improving Attention-based Re-ranking via Attention Re-weighting](https://md.research.elamur.ai/free/en/papers/tian-2026-reattn-improving-attention-based-re/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuxing Tian, Fengran Mo, Weixu Zhang, et al.*2026 · 1 citation*
102. **[The Thin Line Between Comprehension and Persuasion in LLMs](https://md.research.elamur.ai/free/en/papers/wynter-2026-thin-line-between-comprehension-persuasion/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Adrian de Wynter, Tangming Yuan*2026 · 1 citation*
103. **[When LLMs Annotate: Reliability Challenges in Low-Resource NLI](https://md.research.elamur.ai/free/en/papers/panahi-2026-when-llms-annotate-reliability-challenges/) [Workshop on Language Models for Low-Resource Languages](https://md.research.elamur.ai/free/en/venues/loreslm/)**Solmaz Panahi, John Kelleher, Vasudevan Nedumpozhimana*2026 · 1 citation*
104. **[Whose Facts Win? LLM Source Preferences under Knowledge Conflicts](https://md.research.elamur.ai/free/en/papers/schuster-2026-whose-facts-win-llm-source/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jakob Schuster, Vagrant Gautam, Katja Markert*2026 · 1 citation*
105. **[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*
106. **[ANCHOR: LLM-driven Subject Conditioning for Text-to-Image Synthesis](https://md.research.elamur.ai/free/en/papers/ramakrishnan-2026-anchor-llm-driven-subject-conditioning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Aashish Anantha Ramakrishnan, Sharon X Huang, Dongwon Lee*2026*
107. **[Are Social Biases in LLMs Consistent across Generative Tasks? A Case Study for Basque](https://md.research.elamur.ai/free/en/papers/zulaika-2026-are-social-biases-llms-consistent/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Muitze Zulaika, Xabier Saralegi, Julia Shershneva, et al.*2026*
108. **[AYN: A Tiny Yet Competitive Indian Legal Language Model Pretrained from Scratch](https://md.research.elamur.ai/free/en/papers/niyogi-2026-ayn-tiny-yet-competitive-indian/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Mitodru Niyogi, Eric Gaussier, Arnab Bhattacharya*2026*
109. **[Beyond Bias Scores: Unmasking Vacuous Neutrality in Small Language Models](https://md.research.elamur.ai/free/en/papers/manduru-2026-beyond-bias-scores-unmasking-vacuous/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Sumanth Manduru, Carlotta Domeniconi*2026*
110. **[Bias Dynamics in BabyLMs: Towards a Compute-Efficient Sandbox for Democratising Pre-Training Debiasing](https://md.research.elamur.ai/free/en/papers/trhlik-2026-bias-dynamics-babylms-compute-efficient/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Filip Trhlík, Andrew Caines, Paula Buttery*2026*
111. **[BiasGRPO: Stabilizing Bias Mitigation in High-Variance Reward Landscapes via Group-Relative Policy Optimization](https://md.research.elamur.ai/free/en/papers/reddy-2026-biasgrpo-stabilizing-bias-mitigation-high/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Saket Reddy, Ke Yang, ChengXiang Zhai*2026*
112. **[Breaking the Benchmark: Revealing LLM Bias via Minimal Contextual Augmentation](https://md.research.elamur.ai/free/en/papers/miandoab-2026-breaking-benchmark-revealing-llm-bias/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Kaveh Eskandari Miandoab, Mahammed Kamruzzaman, Arshia Gharooni, et al.*2026*
113. **[Colorism in Multimodal AI: An Empirical Exploration of Socioeconomic Linguistic Bias in Text-to-Image Generation](https://md.research.elamur.ai/free/en/papers/maurya-2026-colorism-multimodal-ai-empirical-exploration/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Raj Gaurav Maurya, Vaibhav Shukla, Sreedath Panat*2026*
114. **[DART: Mitigating Harm Drift in Difference-Aware LLMs via Distill-Audit-Repair Training](https://md.research.elamur.ai/free/en/papers/pan-2026-dart-mitigating-harm-drift-difference/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ziwen Pan, Zihan Liang, Jad Kabbara, et al.*2026*
115. **[Detection of Adversarial Prompts with Model Predictive Entropy](https://md.research.elamur.ai/free/en/papers/rubenbauer-2026-detection-adversarial-prompts-model-predictive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Franziska Rubenbauer, Sebastian Steindl, Patrick Levi, et al.*2026*
116. **[Evaluating LLMs for Detecting Demographic-Targeted Social Bias: A Comprehensive Benchmark Study](https://md.research.elamur.ai/free/en/papers/majumdar-2026-evaluating-llms-detecting-demographic-targeted/) [Workshop of Identity Aware AI](https://md.research.elamur.ai/free/en/venues/iaai/)**Ayan Majumdar, Feihao Chen, Jinghui Li, et al.*2026*
117. **[Evaluating the Impact of Source Diversity for RAG in Historical Research](https://md.research.elamur.ai/free/en/papers/mahadeshwar-2026-evaluating-impact-source-diversity-rag/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Ruhi Mahadeshwar, Andreas van Cranenburgh, Tommaso Caselli, et al.*2026*
118. [**Evaluating the Usage of African-American Vernacular English in Large Language Models arXiv:2602.21485**Deja Dunlap, R. Thomas McCoy*2026*](https://md.research.elamur.ai/free/en/papers/dunlap-2026-evaluating-usage-african-american-vernacular/)
119. **[FedReFT: Federated Representation Fine-Tuning with All-But-Me Aggregation](https://md.research.elamur.ai/free/en/papers/siddika-2026-fedreft-federated-representation-fine-tuning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Fatema Siddika, Md Anwar Hossen, Juan Pablo Munoz, et al.*2026*
120. **[FINEST: Improving LLM Responses to Sensitive Topics Through Fine-Grained Evaluation](https://md.research.elamur.ai/free/en/papers/oh-2026-finest-improving-llm-responses-sensitive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Juhyun Oh, Nayeon Lee, Chani Jung, et al.*2026*
121. **[From a River in Gilead to the Inference Distributions of Large Language Models: Covert Dialect Bias and Linguistic Profiling at Scale](https://md.research.elamur.ai/free/en/papers/abdullah-2026-river-gilead-inference-distributions-large/) [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/)**Chowdhury Mohammad Abdullah, Rita Orji*2026*
122. **[Gender Disparities in LLM-Based Intimate Partner Violence Detection](https://md.research.elamur.ai/free/en/papers/prama-2026-gender-disparities-llm-based-intimate/) [Workshop on Natural Language Processing and Computational Social Science](https://md.research.elamur.ai/free/en/venues/nlpcss/)**Tabia Tanzin Prama, Mikaela Irene Fudolig, Abigail M. Crocker, et al.*2026*
123. **[GPT-NL Public Corpus: A Permissively Licensed, Dutch-First Dataset for LLM Pre-training](https://md.research.elamur.ai/free/en/papers/oort-2026-gpt-nl-public-corpus-permissively/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Jesse J. Van Oort, Frank Brinkkemper, Erik de Graaf, et al.*2026*
124. **[How Far Can Bias Go? Tracing Bias from Pre-Training Data to Alignment](https://md.research.elamur.ai/free/en/papers/thaler-2026-how-far-can-bias-go/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Marion Thaler, Abdullatif Köksal, Alina Leidinger, et al.*2026*
125. **[Investigating Gender Stereotypes in Large Language Models via Social Determinants of Health](https://md.research.elamur.ai/free/en/papers/ngo-2026-investigating-gender-stereotypes-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Trung Hieu Ngo, Adrien Bazoge, Solen Quiniou, et al.*2026*
126. [**JudgeMeNot: Personalizing Large Language Models to Emulate Judicial Reasoning in Hebrew**Itay Razumenko, Arnon Sturm, Nir Grinberg*2026*](https://md.research.elamur.ai/free/en/papers/razumenko-2026-judgemenot-personalizing-large-language-models/)
127. **[Lexical Innovation in Business Colour Idioms: Evidence from Large Language Models in Five Languages](https://md.research.elamur.ai/free/en/papers/oleskeviciene-2026-lexical-innovation-business-colour-idioms/) [Workshop Neology and Large Language Models](https://md.research.elamur.ai/free/en/venues/neollm/)**Giedre Valunaite Oleskeviciene, Ágnes Abuczki, Ganit Richter, et al.*2026*
128. **[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*
129. **[Octopus: Gated Selective Attention for Memory-Bounded Long-Context Inference in Large Language Models](https://md.research.elamur.ai/free/en/papers/nguyen-2026-octopus-gated-selective-attention-memory/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chien Van Nguyen, Ryan A. Rossi, Linh Ngo Van, et al.*2026*
130. **[On the Cultural Anachronism and Temporal Reasoning in Vision Language Models](https://md.research.elamur.ai/free/en/papers/ranjan-2026-cultural-anachronism-temporal-reasoning-vision/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mukul Ranjan, Prince Jha, Khushboo Kumari, et al.*2026*
131. **[Optimizer Choice and Calibration for QARiB on Arabic-Script Social Media Offensive Language Detection](https://md.research.elamur.ai/free/en/papers/elshokry-2026-optimizer-choice-calibration-qarib-arabic/) [Workshop on NLP for Languages Using Arabic Script](https://md.research.elamur.ai/free/en/venues/abjadnlp/)**Auda Elshokry, Mohammed Alhanjouri*2026*
132. **[OrthoEdit: Principled and Stable Knowledge Editing via Orthogonal Subspace Projection](https://md.research.elamur.ai/free/en/papers/qiao-2026-orthoedit-principled-stable-knowledge-editing/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Shanbao Qiao, Xuebing Liu, Akshat Gupta, et al.*2026*
133. **[Probing Social Identity Bias in Chinese LLMs with Gendered Pronouns and Social Groups](https://md.research.elamur.ai/free/en/papers/liu-2026-probing-social-identity-bias-chinese/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Geng Liu, Li Feng, Junjie Mu, et al.*2026*
134. **[Probing the Plasticity and Correlation of LLM Value Systems: LLM Value Rankings are Not Stable](https://md.research.elamur.ai/free/en/papers/tang-2026-probing-plasticity-correlation-llm-value/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhenheng Tang, Qihua Pan, Jingya Shen, et al.*2026*
135. **[Quantifying Metric and Model Agreement in Bias Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/asgari-2026-quantifying-metric-model-agreement-bias/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Arash Asgari, Huan Wu, Amirreza Naziri, et al.*2026*
136. **[QueerGen: How LLMs Reflect Societal Norms on Gender and Sexuality in Sentence Completion Task](https://md.research.elamur.ai/free/en/papers/sosto-2026-queergen-how-llms-reflect-societal/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mae Sosto, Delfina S. Martinez Pandiani, Laura Hollink*2026*
137. **[Rethinking Research on Stereotypes: An Analysis through Social Psychological and Computational Perspectives](https://md.research.elamur.ai/free/en/papers/shejole-2026-rethinking-research-stereotypes-analysis-through/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kaustubh Shivshankar Shejole, Pushpak Bhattacharyya*2026*
138. **[Safe-FedLLM: Delving into the Safety of Federated Large Language Models](https://md.research.elamur.ai/free/en/papers/tao-2026-safe-fedllm-delving-into-safety/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mingxiang Tao, Yu Tian, Wenxuan Tu, et al.*2026*
139. **[Thesis Proposal: A Multi-Agent System for Ontology-Based Perspective-Aware Knowledge Extraction](https://md.research.elamur.ai/free/en/papers/miranda-2026-thesis-proposal-multi-agent-system/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Luiz Do Valle Miranda, Grzegorz J. Nalepa*2026*
140. **[Towards Reliable AI Fairness: Challenges in Steering Features within Bias-Implicated Neurons](https://md.research.elamur.ai/free/en/papers/munoz-2026-reliable-ai-fairness-challenges-steering/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Ismael Garrido-Munoz, Arturo Montejo-Raez, Fernando Martínez-Santiago*2026*
141. **[TrajGuard: Streaming Hidden-state Trajectory Detection for Decoding-time Jailbreak Defense](https://md.research.elamur.ai/free/en/papers/liu-2026-trajguard-streaming-hidden-state-trajectory/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Cheng Liu, Xiaolei Liu, Xingyu Li, et al.*2026*
142. **[TryggLLM: A Benchmark for Evaluating LLM Safety in Norwegian](https://md.research.elamur.ai/free/en/papers/touileb-2026-tryggllm-benchmark-evaluating-llm-safety/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Samia Touileb, Truls Pedersen, Isabell Stinessen Haugen*2026*
143. **[Uncovering Hidden Violent Tendencies in LLMs: A Demographic Analysis via Behavioral Vignettes](https://md.research.elamur.ai/free/en/papers/myers-2026-uncovering-hidden-violent-tendencies-llms/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Quintin Myers, Yanjun Gao*2026*
144. **[VIGiA: Instructional Video Guidance via Dialogue Reasoning and Retrieval](https://md.research.elamur.ai/free/en/papers/silva-2026-vigia-instructional-video-guidance-dialogue/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Diogo Glória-Silva, David Semedo, Joao Magalhaes*2026*
145. [**WinoQueer-NL: Assessing Bias in Dutch Language Models toward LGBTQ+ Identities arXiv:2609.02651**Jiska Beuk, Gerasimos Spanakis*2026*](https://md.research.elamur.ai/free/en/papers/beuk-2026-winoqueer-nl-assessing-bias-dutch/)
146. **[“Sorry, Can't Help You”: How Large Language Models Judge Failures to Help Across Languages](https://md.research.elamur.ai/free/en/papers/nair-2026-sorry-can-t-help-you/) [Workshop on Cross-Cultural Considerations in NLP](https://md.research.elamur.ai/free/en/venues/c3nlp/)**Pavithra P M Nair, Gilad Gressel, Krishnashree Achuthan*2026*
147. **[“Yuki Gets Sushi, David Gets Steak?”: Uncovering Gender and Racial Biases in LLM-Based Meal Recommendations](https://md.research.elamur.ai/free/en/papers/wei-2026-yuki-gets-sushi-david-gets/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Xuefeng Wei, Xuan Zhou, Yusuke Sakai, et al.*2026*

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