{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T19:18:38Z","timestamp":1772651918517,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":57,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,2,22]]},"DOI":"10.1145\/3773966.3777920","type":"proceedings-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T17:50:01Z","timestamp":1771264201000},"page":"955-964","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Privacy-preserved LLM Cascade via CoT-enhanced Policy Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4783-6705","authenticated-orcid":false,"given":"Kai","family":"Zhang","sequence":"first","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1318-7180","authenticated-orcid":false,"given":"Congchao","family":"Wang","sequence":"additional","affiliation":[{"name":"Google AIR, Mountain View, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4764-4844","authenticated-orcid":false,"given":"Liqian","family":"Peng","sequence":"additional","affiliation":[{"name":"Google AIR, Mountain View, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2641-5948","authenticated-orcid":false,"given":"Alec","family":"Go","sequence":"additional","affiliation":[{"name":"Google AIR, Mountaion View, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3477-8323","authenticated-orcid":false,"given":"Xiaozhong","family":"Liu","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,2,21]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al.","author":"Achiam Josh","year":"2023","unstructured":"Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al., 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)."},{"key":"e_1_3_2_1_2_1","first-page":"351","article-title":"Vidur: A Large-Scale Simulation Framework For LLM Inference","volume":"6","author":"Agrawal Amey","year":"2024","unstructured":"Amey Agrawal, Nitin Kedia, Jayashree Mohan, Ashish Panwar, Nipun Kwatra, Bhargav Gulavani, Ramachandran Ramjee, and Alexey Tumanov. 2024. Vidur: A Large-Scale Simulation Framework For LLM Inference. Proceedings of Machine Learning and Systems, Vol. 6 (2024), 351-366.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_2_1_3_1","volume-title":"Model Cascading for Code: Reducing Inference Costs with Model Cascading for LLM Based Code Generation. arXiv preprint arXiv:2405.15842","author":"Chen Boyuan","year":"2024","unstructured":"Boyuan Chen, Mingzhi Zhu, Brendan Dolan-Gavitt, Muhammad Shafique, and Siddharth Garg. 2024. Model Cascading for Code: Reducing Inference Costs with Model Cascading for LLM Based Code Generation. arXiv preprint arXiv:2405.15842 (2024)."},{"key":"e_1_3_2_1_4_1","volume-title":"Frugalgpt: How to use large language models while reducing cost and improving performance. arXiv preprint arXiv:2305.05176","author":"Chen Lingjiao","year":"2023","unstructured":"Lingjiao Chen, Matei Zaharia, and James Zou. 2023a. Frugalgpt: How to use large language models while reducing cost and improving performance. arXiv preprint arXiv:2305.05176 (2023)."},{"key":"e_1_3_2_1_5_1","volume-title":"Workshop on efficient systems for foundation models@ ICML2023","author":"Chen Lingjiao","year":"2023","unstructured":"Lingjiao Chen, Matei Zaharia, and James Zou. 2023b. Less is More: Using Multiple LLMs for Applications with Lower Costs. In Workshop on efficient systems for foundation models@ ICML2023."},{"key":"e_1_3_2_1_6_1","unstructured":"Karl Cobbe Vineet Kosaraju Mohammad Bavarian Mark Chen Heewoo Jun Lukasz Kaiser Matthias Plappert Jerry Tworek Jacob Hilton Reiichiro Nakano et al. 2021. Training verifiers to solve math word problems 2021. URL https:\/\/arxiv.org\/abs\/2110.14168 (2021)."},{"key":"e_1_3_2_1_7_1","volume-title":"Security and privacy challenges of large language models: A survey. arXiv preprint arXiv:2402.00888","author":"Das Badhan Chandra","year":"2024","unstructured":"Badhan Chandra Das, M Hadi Amini, and Yanzhao Wu. 2024. Security and privacy challenges of large language models: A survey. arXiv preprint arXiv:2402.00888 (2024)."},{"key":"e_1_3_2_1_8_1","volume-title":"Whispers in the Machine: Confidentiality in LLM-integrated Systems. arXiv preprint arXiv:2402.06922","author":"Evertz Jonathan","year":"2024","unstructured":"Jonathan Evertz, Merlin Chlosta, Lea Sch\u00f6nherr, and Thorsten Eisenhofer. 2024. Whispers in the Machine: Confidentiality in LLM-integrated Systems. arXiv preprint arXiv:2402.06922 (2024)."},{"key":"e_1_3_2_1_9_1","volume-title":"Hyokun Yun, Choon Hui Teo, and Sravan Babu Bodapati.","author":"Feng Qizhang","year":"2024","unstructured":"Qizhang Feng, Siva Rajesh Kasa, Hyokun Yun, Choon Hui Teo, and Sravan Babu Bodapati. 2024. Exposing privacy gaps: Membership inference attack on preference data for LLM alignment. arXiv preprint arXiv:2407.06443 (2024)."},{"key":"e_1_3_2_1_10_1","unstructured":"Tom Gunter Zirui Wang Chong Wang Ruoming Pang Andy Narayanan Aonan Zhang Bowen Zhang Chen Chen Chung-Cheng Chiu David Qiu et al. 2024. Apple intelligence foundation language models. arXiv preprint arXiv:2407.21075 (2024)."},{"key":"e_1_3_2_1_11_1","volume-title":"Aditya Krishna Menon, and Sanjiv Kumar.","author":"Gupta Neha","year":"2024","unstructured":"Neha Gupta, Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Aditya Krishna Menon, and Sanjiv Kumar. 2024. Language Model Cascades: Token-level uncertainty and beyond. arXiv preprint arXiv:2404.10136 (2024)."},{"key":"e_1_3_2_1_12_1","volume-title":"Tryage: Real-time, intelligent routing of user prompts to large language model. arXiv preprint arXiv:2308.11601","author":"Hari Surya Narayanan","year":"2023","unstructured":"Surya Narayanan Hari and Matt Thomson. 2023. Tryage: Real-time, intelligent routing of user prompts to large language model. arXiv preprint arXiv:2308.11601 (2023)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Florian Hartmann Duc-Hieu Tran Peter Kairouz Victor C\u0103rbune et al. 2024. Can LLMs get help from other LLMs without revealing private information? arXiv preprint arXiv:2404.01041 (2024).","DOI":"10.18653\/v1\/2024.privatenlp-1.12"},{"key":"e_1_3_2_1_14_1","volume-title":"MARS: A Benchmark for Multi-LLM Algorithmic Routing System. In ICLR 2024 Workshop: How Far Are We From AGI.","author":"Hu Qitian Jason","year":"2024","unstructured":"Qitian Jason Hu, Jacob Bieker, Xiuyu Li, Nan Jiang, Benjamin Keigwin, Gaurav Ranganath, Kurt Keutzer, and Shriyash Kaustubh Upadhyay. 2024a. MARS: A Benchmark for Multi-LLM Algorithmic Routing System. In ICLR 2024 Workshop: How Far Are We From AGI."},{"key":"e_1_3_2_1_15_1","volume-title":"ROUTERBENCH: A Benchmark for Multi-LLM Routing System. arXiv preprint arXiv:2403.12031","author":"Hu Qitian Jason","year":"2024","unstructured":"Qitian Jason Hu, Jacob Bieker, Xiuyu Li, Nan Jiang, Benjamin Keigwin, Gaurav Ranganath, Kurt Keutzer, and Shriyash Kaustubh Upadhyay. 2024b. ROUTERBENCH: A Benchmark for Multi-LLM Routing System. arXiv preprint arXiv:2403.12031 (2024)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS46773.2023.10182218"},{"key":"e_1_3_2_1_17_1","unstructured":"Balder Janryd and Tim Johansson. 2024. Preventing Health Data from Leaking in a Machine Learning System: Implementing code analysis with LLM and model privacy evaluation testing."},{"key":"e_1_3_2_1_18_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Jitkrittum Wittawat","year":"2024","unstructured":"Wittawat Jitkrittum, Neha Gupta, Aditya K Menon, Harikrishna Narasimhan, Ankit Rawat, and Sanjiv Kumar. 2024. When does confidence-based cascade deferral suffice? Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/5919717"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.265"},{"key":"e_1_3_2_1_21_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Kim Siwon","year":"2024","unstructured":"Siwon Kim, Sangdoo Yun, Hwaran Lee, Martin Gubri, Sungroh Yoon, and Seong Joon Oh. 2024. Propile: Probing privacy leakage in large language models. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_1_22_1","volume-title":"Orchestrallm: Efficient orchestration of language models for dialogue state tracking. arXiv preprint arXiv:2311.09758","author":"Lee Chia-Hsuan","year":"2023","unstructured":"Chia-Hsuan Lee, Hao Cheng, and Mari Ostendorf. 2023. Orchestrallm: Efficient orchestration of language models for dialogue state tracking. arXiv preprint arXiv:2311.09758 (2023)."},{"key":"e_1_3_2_1_23_1","volume-title":"Rethinking classification and localization for cascade R-CNN. arXiv preprint arXiv:1907.11914","author":"Li Ang","year":"2019","unstructured":"Ang Li, Xue Yang, and Chongyang Zhang. 2019. Rethinking classification and localization for cascade R-CNN. arXiv preprint arXiv:1907.11914 (2019)."},{"key":"e_1_3_2_1_24_1","volume-title":"Information Security Planning: A Practical Approach","author":"Lincke Susan","unstructured":"Susan Lincke. 2024. Complying with HIPAA and HITECH. In Information Security Planning: A Practical Approach. Springer, 345-365."},{"key":"e_1_3_2_1_25_1","unstructured":"Sijia Liu Yuanshun Yao Jinghan Jia Stephen Casper Nathalie Baracaldo Peter Hase Xiaojun Xu Yuguang Yao Hang Li Kush R Varshney et al. 2024b. Rethinking machine unlearning for large language models. arXiv preprint arXiv:2402.08787 (2024)."},{"key":"e_1_3_2_1_26_1","volume-title":"Towards safer large language models through machine unlearning. arXiv preprint arXiv:2402.10058","author":"Liu Zheyuan","year":"2024","unstructured":"Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan, Yijun Tian, and Meng Jiang. 2024a. Towards safer large language models through machine unlearning. arXiv preprint arXiv:2402.10058 (2024)."},{"key":"e_1_3_2_1_27_1","volume-title":"Learning to Refuse: Towards Mitigating Privacy Risks in LLMs. arXiv preprint arXiv:2407.10058","author":"Liu Zhenhua","year":"2024","unstructured":"Zhenhua Liu, Tong Zhu, Chuanyuan Tan, and Wenliang Chen. 2024c. Learning to Refuse: Towards Mitigating Privacy Risks in LLMs. arXiv preprint arXiv:2407.10058 (2024)."},{"key":"e_1_3_2_1_28_1","volume-title":"Decoupled Weight Decay Regularization. In International Conference on Learning Representations.","author":"Loshchilov Ilya","year":"2018","unstructured":"Ilya Loshchilov and Frank Hutter. 2018. Decoupled Weight Decay Regularization. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_29_1","volume-title":"Swaroop Mishra, Pei Zhou, Aditya Gupta, Dheeraj Rajagopal, Karthik Kappaganthu, Yiming Yang, et al.","author":"Madaan Aman","year":"2023","unstructured":"Aman Madaan, Pranjal Aggarwal, Ankit Anand, Srividya Pranavi Potharaju, Swaroop Mishra, Pei Zhou, Aditya Gupta, Dheeraj Rajagopal, Karthik Kappaganthu, Yiming Yang, et al., 2023. Automix: Automatically mixing language models. arXiv preprint arXiv:2310.12963 (2023)."},{"key":"e_1_3_2_1_30_1","unstructured":"Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer. 2017. Automatic differentiation in PyTorch. (2017)."},{"key":"e_1_3_2_1_31_1","volume-title":"PocketLLM: Enabling On-Device Fine-Tuning for Personalized LLMs. arXiv preprint arXiv:2407.01031","author":"Peng Dan","year":"2024","unstructured":"Dan Peng, Zhihui Fu, and Jun Wang. 2024. PocketLLM: Enabling On-Device Fine-Tuning for Personalized LLMs. arXiv preprint arXiv:2407.01031 (2024)."},{"key":"e_1_3_2_1_32_1","unstructured":"Alec Radford Jeffrey Wu Rewon Child David Luan Dario Amodei Ilya Sutskever et al. 2019. Language models are unsupervised multitask learners. OpenAI blog Vol. 1 8 (2019) 9."},{"key":"e_1_3_2_1_33_1","unstructured":"Machel Reid Nikolay Savinov Denis Teplyashin Dmitry Lepikhin Timothy Lillicrap Jean-baptiste Alayrac Radu Soricut Angeliki Lazaridou Orhan Firat Julian Schrittwieser et al. 2024. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXiv preprint arXiv:2403.05530 (2024)."},{"key":"e_1_3_2_1_34_1","volume-title":"Proceedings of the 17th ACM International Conference on Web Search and Data Mining. 606-615","author":"\u0160akota Marija","year":"2024","unstructured":"Marija \u0160akota, Maxime Peyrard, and Robert West. 2024. Fly-swat or cannon? cost-effective language model choice via meta-modeling. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining. 606-615."},{"key":"e_1_3_2_1_35_1","volume-title":"Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347","author":"Schulman John","year":"2017","unstructured":"John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)."},{"key":"e_1_3_2_1_36_1","volume-title":"Towards Optimizing the Costs of LLM Usage. arXiv preprint arXiv:2402.01742","author":"Shekhar Shivanshu","year":"2024","unstructured":"Shivanshu Shekhar, Tanishq Dubey, Koyel Mukherjee, Apoorv Saxena, Atharv Tyagi, and Nishanth Kotla. 2024. Towards Optimizing the Costs of LLM Usage. arXiv preprint arXiv:2402.01742 (2024)."},{"key":"e_1_3_2_1_37_1","volume-title":"Large language model routing with benchmark datasets. arXiv preprint arXiv:2309.15789","author":"Shnitzer Tal","year":"2023","unstructured":"Tal Shnitzer, Anthony Ou, M\u00edrian Silva, Kate Soule, Yuekai Sun, Justin Solomon, Neil Thompson, and Mikhail Yurochkin. 2023. Large language model routing with benchmark datasets. arXiv preprint arXiv:2309.15789 (2023)."},{"key":"e_1_3_2_1_38_1","volume-title":"Juliette Love, et al.","author":"Team Gemma","year":"2024","unstructured":"Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivi\u00e8re, Mihir Sanjay Kale, Juliette Love, et al., 2024. Gemma: Open models based on gemini research and technology. arXiv preprint arXiv:2403.08295 (2024)."},{"key":"e_1_3_2_1_39_1","volume-title":"LLaMA: Open and Efficient Foundation Language Models. arXiv preprint arXiv:2302.13971","author":"Touvron Hugo","year":"2023","unstructured":"Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth\u00e9e Lacroix, Baptiste Rozi\u00e8re, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. LLaMA: Open and Efficient Foundation Language Models. arXiv preprint arXiv:2302.13971 (2023)."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2001.990517"},{"key":"e_1_3_2_1_41_1","volume-title":"Aditya Krishna Menon, and Alec Go.","author":"Wang Congchao","year":"2024","unstructured":"Congchao Wang, Sean Augenstein, Keith Rush, Wittawat Jitkrittum, Harikrishna Narasimhan, Ankit Singh Rawat, Aditya Krishna Menon, and Alec Go. 2024. Cascade-Aware Training of Language Models. arXiv preprint arXiv:2406.00060 (2024)."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3552326.3587438"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"e_1_3_2_1_44_1","volume-title":"Rethinking Chain-of-Thought from the Perspective of Self-Training. arXiv preprint arXiv:2412.10827","author":"Wu Zongqian","year":"2024","unstructured":"Zongqian Wu, Baoduo Xu, Ruochen Cui, Mengmeng Zhan, Xiaofeng Zhu, and Lei Feng. 2024. Rethinking Chain-of-Thought from the Perspective of Self-Training. arXiv preprint arXiv:2412.10827 (2024)."},{"key":"e_1_3_2_1_45_1","volume-title":"Llmcad: Fast and scalable on-device large language model inference. arXiv preprint arXiv:2309.04255","author":"Xu Daliang","year":"2023","unstructured":"Daliang Xu, Wangsong Yin, Xin Jin, Ying Zhang, Shiyun Wei, Mengwei Xu, and Xuanzhe Liu. 2023. Llmcad: Fast and scalable on-device large language model inference. arXiv preprint arXiv:2309.04255 (2023)."},{"key":"e_1_3_2_1_46_1","volume-title":"On-Device Language Models: A Comprehensive Review. arXiv preprint arXiv:2409.00088","author":"Xu Jiajun","year":"2024","unstructured":"Jiajun Xu, Zhiyuan Li, Wei Chen, Qun Wang, Xin Gao, Qi Cai, and Ziyuan Ling. 2024. On-Device Language Models: A Comprehensive Review. arXiv preprint arXiv:2409.00088 (2024)."},{"key":"e_1_3_2_1_47_1","volume-title":"David Henry Mguni, and Jun Wang","author":"Yan Xue","year":"2023","unstructured":"Xue Yan, Yan Song, Xinyu Cui, Filippos Christianos, Haifeng Zhang, David Henry Mguni, and Jun Wang. 2023. Ask more, know better: Reinforce-Learned Prompt Questions for Decision Making with Large Language Models. arXiv preprint arXiv:2310.18127 (2023)."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3662006.3662060"},{"key":"e_1_3_2_1_49_1","volume-title":"Large language model cascades with mixture of thoughts representations for cost-efficient reasoning. arXiv preprint arXiv:2310.03094","author":"Yue Murong","year":"2023","unstructured":"Murong Yue, Jie Zhao, Min Zhang, Liang Du, and Ziyu Yao. 2023. Large language model cascades with mixture of thoughts representations for cost-efficient reasoning. arXiv preprint arXiv:2310.03094 (2023)."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"crossref","unstructured":"Nour Eddine Zekaoui Siham Yousfi Mounia Mikram and Maryem Rhanoui. 2023. Enhancing Large Language Models' Utility for Medical Question-Answering: A Patient Health Question Summarization Approach. In 2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA). IEEE 1-8.","DOI":"10.1109\/SITA60746.2023.10373720"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.naacl-long.132"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.537"},{"key":"e_1_3_2_1_53_1","volume-title":"Enabling on-device llms personalization with smartphone sensing. arXiv preprint arXiv:2407.04418","author":"Zhang Shiquan","year":"2024","unstructured":"Shiquan Zhang, Ying Ma, Le Fang, Hong Jia, Simon D'Alfonso, and Vassilis Kostakos. 2024d. Enabling on-device llms personalization with smartphone sensing. arXiv preprint arXiv:2407.04418 (2024)."},{"key":"e_1_3_2_1_54_1","volume-title":"No Free Lunch Theorem for Privacy-Preserving LLM Inference. arXiv preprint arXiv:2405.20681","author":"Zhang Xiaojin","year":"2024","unstructured":"Xiaojin Zhang, Yulin Fei, Yan Kang, Wei Chen, Lixin Fan, Hai Jin, and Qiang Yang. 2024a. No Free Lunch Theorem for Privacy-Preserving LLM Inference. arXiv preprint arXiv:2405.20681 (2024)."},{"key":"e_1_3_2_1_55_1","volume-title":"The Thirty-eighth Annual Conference on Neural Information Processing Systems.","author":"Zhang Xuechen","unstructured":"Xuechen Zhang, Zijian Huang, Ege Onur Taga, Carlee Joe-Wong, Samet Oymak, and Jiasi Chen. [n.d.]. Efficient Contextual LLM Cascades through Budget-Constrained Policy Learning. In The Thirty-eighth Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_1_56_1","volume-title":"Get Confused Cautioausly: Textual Sequence Memorization Erasure with Selective Entropy Maximization. arXiv preprint arXiv:2408.04983","author":"Zhang Zhaohan","year":"2024","unstructured":"Zhaohan Zhang, Ziquan Liu, and Ioannis Patras. 2024c. Get Confused Cautioausly: Textual Sequence Memorization Erasure with Selective Entropy Maximization. arXiv preprint arXiv:2408.04983 (2024)."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-naacl.163"}],"event":{"name":"WSDM '26:The Nineteenth ACM International Conference on Web Search and Data Mining","location":"Boise ID USA","sponsor":["SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval","SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3773966.3777920","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T17:38:36Z","timestamp":1772645916000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3773966.3777920"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,21]]},"references-count":57,"alternative-id":["10.1145\/3773966.3777920","10.1145\/3773966"],"URL":"https:\/\/doi.org\/10.1145\/3773966.3777920","relation":{},"subject":[],"published":{"date-parts":[[2026,2,21]]},"assertion":[{"value":"2026-02-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}