🧑‍🔬 Who cites David Evans

190 citations of their works from 190 papers of our library (of the 149,439 whose bibliographies we read) · 25 with a page here, the most cited first · ← David Evans

1. A Survey of Data Augmentation Approaches for NLParXiv:2105.03075Steven Y. Feng, Varun Gangal, Jason Wei, et al.2021 · 940 citations
2. 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
3. Machine Unlearning of Pre-trained Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Jin Yao, Eli Chien, Minxin Du, et al.2024 · 86 citations
4. Reviewing Scientific Papers for Critical Problems With Reasoning LLMs: Baseline Approaches and Automatic EvaluationarXiv:2505.23824Tianmai M. Zhang, Neil F. Abernethy2025 · 7 citations
5. 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
6. STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Juntong Ni, Shiyu Wang, Qi He, et al.2026 · 3 citations
7. 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
8. Do LLMs Really Memorize Personally Identifiable Information? Revisiting PII Leakage with a Cue-Controlled Memorization FrameworkAnnual Meeting of the Association for Computational Linguistics (ACL)Xiaoyu Luo, Yiyi Chen, Qiongxiu Li, et al.2026 · 1 citation
9. 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
10. Image Corruption-Inspired Membership Inference Attacks against Large Vision-Language ModelsConference of the European Chapter of the Association for Computational Linguistics (EACL)Zongyu Wu, Minhua Lin, Zhiwei Zhang, et al.2026 · 1 citation
11. MALTO at SVELA: A Specific-Attention-Head Approach for Membership Inference Attacks in LLMs Unlearning EvaluationEvaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final WorkshopEvren A. Munis, Mattia Sabato, Erfan Bayat, et al.2026 · 1 citation
12. Optimizing Multilingual LLMs via Federated Learning: A Study of Client Language CompositionLanguage Resources and Evaluation Conference (LREC)Aleix Sant, Jordi Luque, Carlos Escolano2026 · 1 citation
13. Are LLM Benchmarks Already Contaminated? A Systematic Review of Contamination Detection MethodsWorkshop on Generation, Evaluation and Metrics (GEM)Erfan Nourbakhsh, Mohammad Sadegh Sirjani, Amir Mousavi, et al.2026
14. 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
15. 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
16. 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
17. EL-MIA: Quantifying Membership Inference Risks of Sensitive Entities in LLMsLanguage Resources and Evaluation Conference (LREC)Ali Satvaty, Suzan Verberne, Fatih Turkmen2026
18. FedPAGR: Federated Prototype Alignment via Geometric Refinement for Heterogeneous ArchitecturesAnnual Meeting of the Association for Computational Linguistics (ACL)Kris Prasad, Md Abdullah Al Hafiz Khan2026
19. Lexical Popularity: Quantifying the Impact of Pre-training for LLM PerformanceConference of the European Chapter of the Association for Computational Linguistics (EACL)Elena Sofia Ruzzetti, Fabio Massimo Zanzotto, Tommaso Caselli2026
20. 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
21. Obscuring Data Contamination Through Translation: Evidence from Arabic CorporaarXiv:2601.14994Chaymaa Abbas, Nour Shamaa, Mariette Awad2026
22. 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
23. Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data GenerationFindings of the Association for Computational LinguisticsQian Ma, Sarah Rajtmajer2026
24. Safe-FedLLM: Delving into the Safety of Federated Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Mingxiang Tao, Yu Tian, Wenxuan Tu, et al.2026
25. SecureGate: Learning When to Reveal PII Safely via Token-Gated Dual-Adapters for Federated LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Mohamed Shaaban, Mohamed Elmahallawy2026

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