🧑‍🔬 Who cites Anku Rani

133 citations of their works from 133 papers of our library (of the 149,439 whose bibliographies we read) · 22 with a page here, the most cited first · ← Anku Rani

1. A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open QuestionsACM Transactions on Information SystemsLei Huang, Weijiang Yu, Weitao Ma, et al.2023 · 3,231 citations
2. Large Legal Fictions: Profiling Legal Hallucinations in Large Language ModelsarXiv:2401.01301 · Journal of Legal Analysis 16, no. 1 (2024): 64-93Matthew Dahl, Varun Magesh, Mirac Suzgun, et al.2024 · 437 citations
3. A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and ApplicationsarXiv:2402.07927Pranab Sahoo; Ayush Kumar Singh; Sriparna Saha; Vinija Jain; Samrat Mondal;, Aman Chadha2024 · 375 citations
4. Corrective Retrieval Augmented GenerationarXiv:2401.15884Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, et al.2024 · 273 citations
5. LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation MethodsarXiv:2412.05579Haitao Li, Qian Dong, Junjie Chen, et al.2024 · 195 citations
6. Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on GraphsFindings of the Association for Computational LinguisticsBowen Jin, Chulin Xie, Jiawei Zhang, et al.2024 · 89 citations
7. Rethinking Interpretability in the Era of Large Language ModelsarXiv:2402.01761Chandan Singh, Jeevana Priya Inala, Michel Galley, et al.2024 · 87 citations
8. Factuality of Large Language Models: A SurveyConference on Empirical Methods in Natural Language Processing (EMNLP)Yuxia Wang, Minghan Wang, Muhammad Arslan Manzoor, et al.2024 · 77 citations
9. Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-EvaluationAnnual Meeting of the Association for Computational Linguistics (ACL)Xiaoying Zhang, Baolin Peng, Ye Tian, et al.2024 · 69 citations
10. Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMsarXiv:2507.09477Yangning Li, Weizhi Zhang, Yuyao Yang, et al.2025 · 17 citations
11. Sparks of Science: Hypothesis Generation Using Structured Paper DataarXiv:2504.12976Charles O'Neill, Tirthankar Ghosal, Roberta R\uaileanu, et al.2025 · 16 citations
12. MetaFaith: Faithful Natural Language Uncertainty Expression in LLMsConference on Empirical Methods in Natural Language Processing (EMNLP)Gabrielle Kaili-May Liu, Gal Yona, Avi Caciularu, et al.2025 · 6 citations
13. KnowRL: Exploring Knowledgeable Reinforcement Learning for FactualityAnnual Meeting of the Association for Computational Linguistics (ACL)Baochang Ren, Shuofei Qiao, Ningyu Zhang, et al.2026 · 5 citations
14. ASOS at Arabic LLMs Hallucinations 2024: Can LLMs detect their Hallucinations :)Workshop on Open-Source Arabic Corpora and Processing Tools with Shared TasksSerry Taiseer Sibaee, Abdullah I. Alharbi, Samar Ahmed, et al.2024 · 3 citations
15. CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive DecodingFindings of the Association for Computational LinguisticsXi Zhang, Zaiqiao Meng, Jake Lever, et al.2026 · 2 citations
16. Mechanisms of Prompt-Induced Hallucination in Vision–Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)William Rudman, Michal Golovanevsky, Dana Arad, et al.2026 · 2 citations
17. The Unintended Trade-off of AI Alignment: Balancing Hallucination Mitigation and Safety in LLMsFindings of the Association for Computational LinguisticsOmar Mahmoud, Ali Khalil, Thommen George Karimpanal, et al.2026 · 2 citations
18. Context-Fidelity Boosting: Enhancing Faithful Generation through Watermark-Inspired DecodingFindings of the Association for Computational LinguisticsWeixu Zhang, Fanghua Ye, Qiang Gao, et al.2026 · 1 citation
19. Truth or Mirage? Towards End-To-End Factuality Evaluation with LLM-O asisComputational LinguisticsAlessandro Scirè, Andrei Stefan Bejgu, Simone Tedeschi, et al.2026 · 1 citation
20. GPT-NL Public Corpus: A Permissively Licensed, Dutch-First Dataset for LLM Pre-trainingLanguage Resources and Evaluation Conference (LREC)Jesse J. Van Oort, Frank Brinkkemper, Erik de Graaf, et al.2026
21. PretrainRL: Alleviating Factuality Hallucination of Large Language Models at the BeginningFindings of the Association for Computational LinguisticsLangming Liu, Kangtao Lv, Haibin Chen, et al.2026
22. Survey on Hallucinations in Large Language Models (大语言模型的幻觉问题研究综述)Journal of SoftwareZeyuan Liu (刘泽垣), Pengjiang Wang (王鹏江), Xiaobin Song (宋晓斌), et al.2024

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