--- title: "Who cites Jack Lanchantin — 231 papers of our library" description: "231 papers of the Elamur Research library cite the works of Jack Lanchantin, found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/authors/jack-lanchantin/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/jack-lanchantin/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Jack Lanchantin](https://md.research.elamur.ai/free/en/authors/jack-lanchantin/) / cited by # Who cites Jack Lanchantin 256 citations of their works from 231 papers of our library (of the 141,840 whose bibliographies we read) · 27 with a page here, the most cited first · [← Jack Lanchantin](https://md.research.elamur.ai/free/en/authors/jack-lanchantin/) 1. [**TextBugger: Generating Adversarial Text Against Real-world Applications arXiv:1812.05271**Jinfeng Li, Shouling Ji, Tianyu Du, et al.*2018 · 543 citations*](https://md.research.elamur.ai/free/en/papers/li-2018-textbugger-generating-adversarial-text-against/) **[Word-level Textual Adversarial Attacking as Combinatorial Optimization](https://md.research.elamur.ai/free/en/papers/zang-2020-word-level-textual-adversarial-attacking/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuan Zang, Fanchao Qi, Chenghao Yang, et al.*2020 · 340 citations* [**AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models arXiv:2310.04451**Xiaogeng Liu, Nan Xu, Muhao Chen, et al.*2023 · 334 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-autodan-generating-stealthy-jailbreak-prompts/) [**PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts arXiv:2306.04528**Kaijie Zhu, Jindong Wang, Jiaheng Zhou, et al.*2023 · 303 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-promptrobust-towards-evaluating-robustness-large/) [**Chain-of-Verification Reduces Hallucination in Large Language Models arXiv:2309.11495**Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, et al.*2023 · 299 citations*](https://md.research.elamur.ai/free/en/papers/dhuliawala-2023-chain-verification-reduces-hallucination-large/) **[Contextualized Perturbation for Textual Adversarial Attack](https://md.research.elamur.ai/free/en/papers/li-2021-contextualized-perturbation-textual-adversarial-attack/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Dianqi Li, Yizhe Zhang, Hao Peng, et al.*2021 · 282 citations* [**Baseline Defenses for Adversarial Attacks Against Aligned Language Models arXiv:2309.00614**Neel Jain, Avi Schwarzschild, Yuxin Wen, et al.*2023 · 225 citations*](https://md.research.elamur.ai/free/en/papers/jain-2023-baseline-defenses-adversarial-attacks-against/) **[Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer](https://md.research.elamur.ai/free/en/papers/qi-2021-mind-style-text-adversarial-backdoor/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Fanchao Qi, Yangyi Chen, Xurui Zhang, et al.*2021 · 188 citations* **[Gradient-based Adversarial Attacks against Text Transformers](https://md.research.elamur.ai/free/en/papers/guo-2021-gradient-based-adversarial-attacks-against/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chuan Guo, Alexandre Sablayrolles, Hervé Jégou, et al.*2021 · 187 citations* **[Reevaluating Adversarial Examples in Natural Language](https://md.research.elamur.ai/free/en/papers/morris-2020-reevaluating-adversarial-examples-natural-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**John Morris, Eli Lifland, Jack Lanchantin, et al.*2020 · 151 citations* **[Measure and Improve Robustness in NLP Models: A Survey](https://md.research.elamur.ai/free/en/papers/wang-2022-measure-improve-robustness-nlp-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Xuezhi Wang, Haohan Wang, Diyi Yang*2022 · 148 citations* [**Quiet-STaR: Language models can teach themselves to think before speaking***2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/al-2024-quiet-star-language-models-teach/) **[RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors](https://md.research.elamur.ai/free/en/papers/dugan-2024-raid-shared-benchmark-robust-evaluation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Liam Dugan, Alyssa Hwang, Filip Trhlík, et al.*2024 · 110 citations* **[Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM](https://md.research.elamur.ai/free/en/papers/cao-2024-defending-against-alignment-breaking-attacks/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Bochuan Cao, Yuanpu Cao, Lu Lin, et al.*2024 · 107 citations* **[Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment](https://md.research.elamur.ai/free/en/papers/raina-2024-is-llm-as-judge-robust/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Vyas Raina, Adian Liusie, Mark Gales*2024 · 85 citations* [**Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks arXiv:2310.10844**Erfan Shayegani, Md Abdullah Al Mamun, Yu Fu, et al.*2023 · 83 citations*](https://md.research.elamur.ai/free/en/papers/shayegani-2023-survey-vulnerabilities-large-language-models/) [**A Comprehensive Survey on Long Context Language Modeling arXiv:2503.17407**Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-comprehensive-survey-long-context-language/) [**Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples**Xiaoqing Zheng, Jiehang Zeng, Yi Zhou, et al.*2020 · 54 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2020-evaluating-enhancing-robustness-neural-network/) [**A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples**Yuxuan Wang, Wanxiang Che, Ivan Titov, et al.*2021 · 8 citations*](https://md.research.elamur.ai/free/en/papers/wang-2021-closer-look-into-robustness-neural/) [**Evaluation of Adversarial Robustness in Arabic Language Models arXiv:2607.25814**Anwar Alajmi, Ayed Salman, Imtiaz Ahmad*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/alajmi-2026-evaluation-adversarial-robustness-arabic-language/) **[SPARTA: Evaluating Reasoning Segmentation Robustness through Black-Box Adversarial Paraphrasing in Text Autoencoder Latent Space](https://md.research.elamur.ai/free/en/papers/zinkovich-2026-sparta-evaluating-reasoning-segmentation-robustness/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Viktoriia Zinkovich, Anton Antonov, Andrei Spiridonov, et al.*2026* **[Robustness Evaluation of ChatGPT Against Chinese Adversarial Attacks (中文对抗攻击下的ChatGPT鲁棒性评估)](https://md.research.elamur.ai/free/en/papers/zhang-2025-robustness-evaluation-chatgpt/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Yunting Zhang (张云婷), Lin Ye (叶麟), Baisong Li (李柏松), et al.*2025* 204 more citing papers of our library have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/authors/jack-lanchantin/) — Jack Lanchantin