🧑‍🔬 Who cites Pranav Narayanan Venkit

75 citations of their works from 75 papers of our library (of the 149,439 whose bibliographies we read) · 22 with a page here, the most cited first · ← Pranav Narayanan Venkit

1. Bias and Fairness in Large Language Models: A SurveyComputational LinguisticsIsabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al.2023 · 1,082 citations
2. From Persona to Personalization: A Survey on Role-Playing Language AgentsarXiv:2404.18231Jiangjie Chen, Xintao Wang, Rui Xu, et al.2024 · 115 citations
3. AgentReview: Exploring Peer Review Dynamics with LLM AgentsConference on Empirical Methods in Natural Language Processing (EMNLP)Yiqiao Jin, Qinlin Zhao, Yiyang Wang, et al.2024 · 86 citations
4. CycleResearcher: Improving Automated Research via Automated ReviewarXiv:2411.00816Yixuan Weng, Minjun Zhu, Guangsheng Bao, et al.2024 · 65 citations
5. The Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce HarmConference on Empirical Methods in Natural Language Processing (EMNLP)Aakanksha, Arash Ahmadian, Beyza Ermis, et al.2024 · 49 citations
6. AI4Research: A Survey of Artificial Intelligence for Scientific ResearchScience China Information SciencesQiguang Chen, Mingda Yang, Libo Qin, et al.2025 · 21 citations
7. TreeReview: A Dynamic Tree of Questions Framework for Deep and Efficient LLM-based Scientific Peer ReviewConference on Empirical Methods in Natural Language Processing (EMNLP)Yuan Chang, Ziyue Li, Hengyuan Zhang, et al.2025 · 16 citations
8. Mind the Blind Spots: A Focus-Level Evaluation Framework for LLM ReviewsConference on Empirical Methods in Natural Language Processing (EMNLP)Hyungyu Shin, Jingyu Tang, Yoonjoo Lee, et al.2025 · 14 citations
9. LLM-Based Human-Agent Collaboration and Interaction Systems: A SurveyFindings of the Association for Computational LinguisticsHenry Peng Zou, Wei-Chieh Huang, Yaozu Wu, et al.2026 · 12 citations
10. Can LLMs Identify Critical Limitations within Scientific Research? A Systematic Evaluation on AI Research PapersAnnual Meeting of the Association for Computational Linguistics (ACL)Zhijian Xu, Yilun Zhao, Manasi Patwardhan, et al.2025 · 11 citations
11. AbGen: Evaluating Large Language Models in Ablation Study Design and Evaluation for Scientific ResearchAnnual Meeting of the Association for Computational Linguistics (ACL)Yilun Zhao, Weiyuan Chen, Zhijian Xu, et al.2025 · 9 citations
12. Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer ReviewsFindings of the Association for Computational LinguisticsSai Suresh Macharla Vasu, Ivaxi Sheth, Hui-Po Wang, et al.2026 · 8 citations
13. Reviewing Scientific Papers for Critical Problems With Reasoning LLMs: Baseline Approaches and Automatic EvaluationarXiv:2505.23824Tianmai M. Zhang, Neil F. Abernethy2025 · 7 citations
14. Automatic Reviewers Fail to Detect Faulty Reasoning in Research Papers: A New Counterfactual Evaluation FrameworkTransactions of the Association for Computational Linguistics (TACL)Nils Dycke, Iryna Gurevych2026 · 5 citations
15. NovBench: Evaluating Large Language Models on Academic Paper Novelty AssessmentFindings of the Association for Computational LinguisticsWenqing Wu, Yi Zhao, Yuzhuo Wang, et al.2026 · 4 citations
16. Adapting Psycholinguistic Research for LLMs: Gender-inclusive Language in a Coreference ContextWorkshop on Gender Bias in Natural Language ProcessingMarion Bartl, Thomas Brendan Murphy, Susan Leavy2025 · 3 citations
17. Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the FutureAnnual Meeting of the Association for Computational Linguistics (ACL)Sihong Wu, Owen Jiang, Yilun Zhao, et al.2026 · 1 citation
18. Bias Dynamics in BabyLMs: Towards a Compute-Efficient Sandbox for Democratising Pre-Training DebiasingFindings of the Association for Computational LinguisticsFilip Trhlík, Andrew Caines, Paula Buttery2026
19. From Isolated Scoring to Collaborative Ranking: A Comparison-Native Framework for LLM-Based Paper EvaluationFindings of the Association for Computational LinguisticsPujun Zheng, Jiacheng Yao, Jinquan Zheng, et al.2026
20. How Far Can Bias Go? Tracing Bias from Pre-Training Data to AlignmentLanguage Resources and Evaluation Conference (LREC)Marion Thaler, Abdullatif Köksal, Alina Leidinger, et al.2026
21. Implicit Bias in Peer Review: Through the Lens of Language AbstractionLanguage Resources and Evaluation Conference (LREC)Xulang Zhang, Rui Mao, Erik Cambria2026
22. Preference Optimization for Review Question Generation Improves Writing QualityFindings of the Association for Computational LinguisticsKarun Sharma, Vidushee Vats, Shengzhi LI, et al.2026

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