🧑‍🔬 Who cites Somesh Jha

119 citations of their works from 119 papers of our library (of the 149,439 whose bibliographies we read) · 29 with a page here, the most cited first · ← Somesh Jha

1. Extracting Training Data from Large Language ModelsarXiv:2012.07805Nicholas Carlini, Florian Tramer, Eric Wallace, et al.2020 · 955 citations
2. The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural NetworksarXiv:1802.08232Nicholas Carlini, Chang Liu, Ulfar Erlingsson, et al.2018 · 692 citations
3. Security and Privacy Challenges of Large Language Models: A SurveyACM Computing SurveysBadhan Chandra Das, M. Hadi Amini, Yanzhao Wu2024 · 424 citations
4. LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation DatasetarXiv:2309.11998Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, et al.2023 · 242 citations
5. Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' AlignmentarXiv:2308.05374Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li2023 · 223 citations
6. Scalable Extraction of Training Data from (Production) Language ModelsarXiv:2311.17035Milad Nasr, Nicholas Carlini, Jonathan Hayase, et al.2023 · 211 citations
7. Knowledge Conflicts for LLMs: A SurveyConference on Empirical Methods in Natural Language Processing (EMNLP)Rongwu Xu, Zehan Qi, Zhijiang Guo, et al.2024 · 182 citations
8. Deduplicating Training Data Mitigates Privacy Risks in Language ModelsarXiv:2202.06539Nikhil Kandpal, Eric Wallace, Colin Raffel2022 · 172 citations
9. Membership Inference Attacks against Language Models via Neighbourhood ComparisonFindings of the Association for Computational LinguisticsJustus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, et al.2023 · 165 citations
10. Quantifying Privacy Risks of Masked Language Models Using Membership Inference AttacksConference on Empirical Methods in Natural Language Processing (EMNLP)Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal, et al.2022 · 146 citations
11. Do Membership Inference Attacks Work on Large Language Models?arXiv:2402.07841Michael Duan, Anshuman Suri, Niloofar Mireshghallah, et al.2024 · 87 citations
12. Survey of Vulnerabilities in Large Language Models Revealed by Adversarial AttacksarXiv:2310.10844Erfan Shayegani, Md Abdullah Al Mamun, Yu Fu, et al.2023 · 83 citations
13. Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?Transactions of the Association for Computational Linguistics (TACL)Sorami Hisamoto, Matt Post, Kevin Duh2020 · 82 citations
14. SOUL: Unlocking the Power of Second-Order Optimization for LLM UnlearningConference on Empirical Methods in Natural Language Processing (EMNLP)Jinghan Jia, Yihua Zhang, Yimeng Zhang, et al.2024 · 46 citations
15. Get Confused Cautiously: Textual Sequence Memorization Erasure with Selective Entropy MaximizationInternational Conference on Computational Linguistics (COLING)Zhaohan Zhang, Ziquan Liu, Ioannis Patras2025 · 7 citations
16. Detecting Training Data of Large Language Models via Expectation MaximizationConference of the European Chapter of the Association for Computational Linguistics (EACL)Gyuwan Kim, Yang Li, Evangelia Spiliopoulou, et al.2026 · 5 citations
17. BLUR: A Bi-Level Optimization Approach for LLM UnlearningConference of the European Chapter of the Association for Computational Linguistics (EACL)Hadi Reisizadeh, Jinghan Jia, Zhiqi Bu, et al.2026 · 4 citations
18. Maximizing Local Entropy Where It Matters: Prefix-Aware Localized LLM UnlearningAnnual Meeting of the Association for Computational Linguistics (ACL)Naixin Zhai, Pengyang Shao, Binbin Zheng, et al.2026 · 2 citations
19. Neural Breadcrumbs: Membership Inference Attacks on LLMs Through Hidden State and Attention Pattern AnalysisConference of the European Chapter of the Association for Computational Linguistics (EACL)Disha Makhija, Manoj Ghuhan Arivazhagan, Vinayshekhar Bannihatti Kumar, et al.2026 · 2 citations
20. Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical AdoptionFindings of the Association for Computational LinguisticsYepeng Liu, Xuandong Zhao, Dawn Song, et al.2026 · 2 citations
21. Hey, That's My Data! Token-Only Dataset Inference in Large Language ModelsFindings of the Association for Computational LinguisticsChen Xiong, Zihao Wang, Rui Zhu, et al.2026 · 1 citation
22. The Model's Language Matters: A Comparative Privacy Analysis of LLMsFindings of the Association for Computational LinguisticsAbhishek Kumar Mishra, Antoine Boutet, Lucas Magnana2026 · 1 citation
23. Black-Box Membership Inference Attacks for Video Training Data in Multimodal Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Jinrui Wang, Zhenfeng Gao, Wendan Wang, et al.2026
24. CheckMIABench: Firm Foundations For Membership Inference Attacks on Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Jeffrey George Wang, Jason Wang, Marvin Li, et al.2026
25. Detecting Non-Membership in LLM Training Data via Rank CorrelationsConference of the European Chapter of the Association for Computational Linguistics (EACL)Pranav Shetty, Mirazul Haque, Zhiqiang Ma, et al.2026
26. Fast-MIA: Efficient and Scalable Membership Inference for LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Hiromu Takahashi, Shotaro Ishihara2026
27. LLMSurgeon: Diagnosing Data Mixture of Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Yaxin Luo, Jiacheng Cui, Xiaohan Zhao, et al.2026
28. PDR: A Plug-and-Play Positional Decay Framework for LLM Pre-training Data DetectionAnnual Meeting of the Association for Computational Linguistics (ACL)Jinhan Liu, Yibo Yang, Ruiying Lu, et al.2026
29. What Are LLMs Doing to Scientific Communication? Measuring Changes in Writing Practices and Reading ExperienceLanguage Resources and Evaluation Conference (LREC)Filip Miletić, Neele Falk2026

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