{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T16:39:21Z","timestamp":1786120761891,"version":"build-2736575974"},"publisher-location":"New York, NY, USA","reference-count":78,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T00:00:00Z","timestamp":1786147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"German Federal Ministry of Education and Research","award":["01IS24082"],"award-info":[{"award-number":["01IS24082"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8,9]]},"DOI":"10.1145\/3770855.3817563","type":"proceedings-article","created":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T01:08:33Z","timestamp":1786064913000},"page":"9546-9557","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Synthetic Interaction Data for Scalable Personalization in Large Language Models"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9900-2822","authenticated-orcid":false,"given":"Yuchen","family":"Ma","sequence":"first","affiliation":[{"name":"Munich Center for Machine Learning & LMU Munich, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8315-7972","authenticated-orcid":false,"given":"Yue","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Notre Dame, South Bend, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3967-1831","authenticated-orcid":false,"given":"Wenjie","family":"Wang","sequence":"additional","affiliation":[{"name":"Independent Researcher, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0551-7032","authenticated-orcid":false,"given":"Xiaonan","family":"Luo","sequence":"additional","affiliation":[{"name":"University of Notre Dame, South Bend, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3574-5665","authenticated-orcid":false,"given":"Xiangliang","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Notre Dame, South Bend, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7856-8729","authenticated-orcid":false,"given":"Stefan","family":"Feuerriegel","sequence":"additional","affiliation":[{"name":"Munich Center for Machine Learning & LMU Munich, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,8,8]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"AI@Meta. 2024. Llama 3 Model Card. (2024). https:\/\/github.com\/meta-llama\/llama3\/blob\/main\/MODEL_CARD.md"},{"key":"e_1_3_2_1_2_1","unstructured":"Anthropic. 2025. Claude Sonnet 4.5. https:\/\/www.anthropic.com\/claude\/sonnet Accessed: 2026-02-06."},{"key":"e_1_3_2_1_3_1","unstructured":"Jacob Austin Augustus Odena Maxwell Nye Maarten Bosma Henryk Michalewski David Dohan Ellen Jiang Carrie Cai Michael Terry Quoc Le et al. 2021. Program synthesis with large language models. arXiv preprint arXiv:2108.07732 (2021)."},{"key":"e_1_3_2_1_4_1","volume-title":"Constitutional AI: Harmlessness from AI feedback. arXiv preprint arXiv:2212.08073","author":"Bai Yuntao","year":"2022","unstructured":"Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al., 2022. Constitutional AI: Harmlessness from AI feedback. arXiv preprint arXiv:2212.08073 (2022)."},{"key":"e_1_3_2_1_5_1","unstructured":"Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in Neural Information Processing Systems V33 (2020) 1877-1901."},{"key":"e_1_3_2_1_6_1","volume-title":"VarXiv:2506.12109","author":"Bu Hyungjune","year":"2025","unstructured":"Hyungjune Bu, Chanjoo Jung, Minjae Kang, and Jaehyung Kim. 2025. Personalized LLM Decoding via Contrasting Personal Preference. arXiv preprint, VarXiv:2506.12109 (2025)."},{"key":"e_1_3_2_1_7_1","volume-title":"VarXiv:2406.20094","author":"Chan Xin","year":"2024","unstructured":"Xin Chan, Xiaoyang Wang, Dian Yu, Haitao Mi, and Dong Yu. 2024. Scaling Synthetic Data Creation with 1,000,000,000 Personas. arXiv preprint, VarXiv:2406.20094 (2024)."},{"key":"e_1_3_2_1_8_1","volume-title":"International Conference on Learning Representations.","author":"Chen Ruizhe","year":"2024","unstructured":"Ruizhe Chen, Xiaotian Zhang, Meng Luo, Wenhao Chai, and Zuozhu Liu. 2024. PAD: Personalized Alignment of LLMs at Decoding-time. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.176"},{"key":"e_1_3_2_1_10_1","volume-title":"Increasing diversity while maintaining accuracy: Text data generation with large language models and human interventions. arXiv preprint arXiv:2306.04140","author":"Young Chung John Joon","year":"2023","unstructured":"John Joon Young Chung, Ece Kamar, and Saleema Amershi. 2023. Increasing diversity while maintaining accuracy: Text data generation with large language models and human interventions. arXiv preprint arXiv:2306.04140 (2023)."},{"key":"e_1_3_2_1_11_1","volume-title":"Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge. arXiv:1803.05457v1","author":"Clark Peter","year":"2018","unstructured":"Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018. Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge. arXiv:1803.05457v1 (2018)."},{"key":"e_1_3_2_1_12_1","volume-title":"Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM.","author":"Conover Mike","year":"2023","unstructured":"Mike Conover, Matt Hayes, Ankit Mathur, Jianwei Xie, Jun Wan, Sam Shah, Ali Ghodsi, Patrick Wendell, Matei Zaharia, and Reynold Xin. 2023. Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM."},{"key":"e_1_3_2_1_13_1","volume-title":"SEE: Strategic Exploration and Exploitation for Cohesive In-Context Prompt Optimization. https:\/\/api.semanticscholar.org\/CorpusID:267750577","author":"Cui Wendi","year":"2024","unstructured":"Wendi Cui, Jiaxin Zhang, Zhuohang Li, Hao Sun, Damien Lopez, Kamalika Das, Bradley Malin, and Kumar Sricharan. 2024. SEE: Strategic Exploration and Exploitation for Cohesive In-Context Prompt Optimization. https:\/\/api.semanticscholar.org\/CorpusID:267750577"},{"key":"e_1_3_2_1_14_1","volume-title":"Auggpt: Leveraging chatgpt for text data augmentation","author":"Dai Haixing","year":"2025","unstructured":"Haixing Dai, Zhengliang Liu, Wenxiong Liao, Xiaoke Huang, Yihan Cao, Zihao Wu, Lin Zhao, Shaochen Xu, Fang Zeng, Wei Liu, et al., 2025. Auggpt: Leveraging chatgpt for text data augmentation. IEEE Transactions on Big Data (2025)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.183"},{"key":"e_1_3_2_1_16_1","unstructured":"Yin Fang Xiaozhuan Liang Ningyu Zhang Kangwei Liu Rui Huang Zhuo Chen Xiaohui Fan and Huajun Chen. 2024. Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models. In ICLR. OpenReview.net. https:\/\/openreview.net\/pdf?id=Tlsdsb6l9n"},{"key":"e_1_3_2_1_17_1","volume-title":"Shan Gao, and Michael R. Lyu.","author":"Gao Shuzheng","year":"2025","unstructured":"Shuzheng Gao, Chaozheng Wang, Cuiyun Gao, Xiaoqian Jiao, Chun Yong Chong, Shan Gao, and Michael R. Lyu. 2025. The Prompt Alchemist: Automated LLM-Tailored Prompt Optimization for Test Case Generation. arXiv preprint, VarXiv:2501.01329 (2025)."},{"key":"e_1_3_2_1_18_1","volume-title":"VarXiv:2505.00038","author":"Garbacea Cristina","year":"2025","unstructured":"Cristina Garbacea and Chenhao Tan. 2025. HyPerAlign: Interpretable Personalized LLM Alignment via Hypothesis Generation. arXiv preprint, VarXiv:2505.00038 (2025)."},{"key":"e_1_3_2_1_19_1","volume-title":"VarXiv:2503.17003","author":"Guan Jian","year":"2025","unstructured":"Jian Guan, Jun Wu, Jia-Nan Li, Chuanqi Cheng, and Wei Wu. 2025. A Survey on Personalized Alignment - The Missing Piece for Large Language Models in Real-World Applications. arXiv preprint, VarXiv:2503.17003 (2025)."},{"key":"e_1_3_2_1_20_1","volume-title":"Tsinghua University, and Microsoft Research","author":"Guo Qingyan","year":"2023","unstructured":"Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, Yujiu Yang, Tsinghua University, and Microsoft Research. 2023. EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers."},{"key":"e_1_3_2_1_21_1","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR)","author":"Hendrycks Dan","year":"2021","unstructured":"Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021. Measuring Massive Multitask Language Understanding. Proceedings of the International Conference on Learning Representations (ICLR) (2021)."},{"key":"e_1_3_2_1_22_1","volume-title":"Lora: Low-rank adaptation of large language models. ICLR, V1, 2","author":"Hu Edward J","year":"2022","unstructured":"Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al., 2022. Lora: Low-rank adaptation of large language models. ICLR, V1, 2 (2022), 3."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Yue Huang Zhengzhe Jiang Xiaonan Luo Kehan Guo Haomin Zhuang Yujun Zhou Zhengqing Yuan Xiaoqi Sun Jules Schleinitz Yanbo Wang et al. 2025a. ChemOrch: Empowering LLMs with Chemical Intelligence via Synthetic Instructions. arXiv preprint arXiv:2509.16543 (2025).","DOI":"10.52202\/085713-0258"},{"key":"e_1_3_2_1_24_1","volume-title":"The Thirteenth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=F5R0lG74Tu","author":"Huang Yue","year":"2025","unstructured":"Yue Huang, Siyuan Wu, Chujie Gao, Dongping Chen, Qihui Zhang, Yao Wan, Tianyi Zhou, Chaowei Xiao, Jianfeng Gao, Lichao Sun, and Xiangliang Zhang. 2025b. DataGen: Unified Synthetic Dataset Generation via Large Language Models. In The Thirteenth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=F5R0lG74Tu"},{"key":"e_1_3_2_1_25_1","volume-title":"Jiashu He, Hanchao Yu, Radha Poovendran, Gregory Wornell, Lyle Ungar, Dan Roth, Sihao Chen, and Camillo Jose Taylor.","author":"Jiang Bowen","year":"2025","unstructured":"Bowen Jiang, Yuan Yuan, Maohao Shen, Zhuoqun Hao, Zhangchen Xu, Zichen Chen, Ziyi Liu, Anvesh Rao Vijjini, Jiashu He, Hanchao Yu, Radha Poovendran, Gregory Wornell, Lyle Ungar, Dan Roth, Sihao Chen, and Camillo Jose Taylor. 2025. PersonaMem-v2: Towards Personalized Intelligence via Learning Implicit User Personas and Agentic Memory. arXiv:2512.06688 [cs.CL]"},{"key":"e_1_3_2_1_26_1","volume-title":"CUPID: Evaluating Personalized and Contextualized Alignment of LLMs from Interactions. arXiv preprint, VarXiv:2508.01674","author":"Kim Tae Soo","year":"2025","unstructured":"Tae Soo Kim, Yoonjoo Lee, Yoonah Park, Jiho Kim, Young-Ho Kim, and Juho Kim. 2025. CUPID: Evaluating Personalized and Contextualized Alignment of LLMs from Interactions. arXiv preprint, VarXiv:2508.01674 (2025)."},{"key":"e_1_3_2_1_27_1","volume-title":"Sotiris Anagnostidis, Zhi Rui Tam, Keith Stevens, Abdullah Barhoum, Duc Nguyen, Oliver Stanley, Rich\u00e1rd Nagyfi, et al.","author":"K\u00f6pf Andreas","year":"2023","unstructured":"Andreas K\u00f6pf, Yannic Kilcher, Dimitri Von R\u00fctte, Sotiris Anagnostidis, Zhi Rui Tam, Keith Stevens, Abdullah Barhoum, Duc Nguyen, Oliver Stanley, Rich\u00e1rd Nagyfi, et al., 2023. Openassistant conversations-democratizing large language model alignment. Advances in neural information processing systems, V36 (2023), 47669-47681."},{"key":"e_1_3_2_1_28_1","unstructured":"Harrison Lee Samrat Phatale Hassan Mansoor Thomas Mesnard Johan Ferret Kellie Lu Colton Bishop Ethan Hall Victor Carbune Abhinav Rastogi et al. 2023. Rlaif vs. rlhf: Scaling reinforcement learning from human feedback with ai feedback. arXiv preprint arXiv:2309.00267."},{"key":"e_1_3_2_1_29_1","volume-title":"Seungone Kim, and Minjoon Seo.","author":"Lee Seongyun","year":"2024","unstructured":"Seongyun Lee, Sue Hyun Park, Seungone Kim, and Minjoon Seo. 2024. Aligning to thousands of preferences via system message generalization. Advances in Neural Information Processing Systems, V37 (2024), 73783-73829."},{"key":"e_1_3_2_1_30_1","volume-title":"Matryoshka Pilot: Learning to Drive Black-Box LLMs with LLMs.","author":"Li Changhao","year":"2024","unstructured":"Changhao Li, Yuchen Zhuang, Rushi Qiang, Haotian Sun, Hanjun Dai, Chao Zhang, and Bo Dai. 2024. Matryoshka Pilot: Learning to Drive Black-Box LLMs with LLMs."},{"key":"e_1_3_2_1_31_1","volume-title":"VarXiv:2503.15463","author":"Li Jia-Nan","year":"2025","unstructured":"Jia-Nan Li, Jian Guan, Songhao Wu, Wei Wu, and Rui Yan. 2025. From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level Alignment. arXiv preprint, VarXiv:2503.15463 (2025)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3711896.3737432"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.229"},{"key":"e_1_3_2_1_34_1","unstructured":"Ruibo Liu Jerry Wei Fangyu Liu Chenglei Si Yanzhe Zhang Jinmeng Rao Steven Zheng Daiyi Peng Diyi Yang Denny Zhou et al. 2024. Best practices and lessons learned on synthetic data. arXiv preprint arXiv:2404.07503 (2024)."},{"key":"e_1_3_2_1_35_1","volume-title":"Guilherme Penedo, Edward Beeching, Quentin Gallou\u00e9dec, Nathan Habib, Lewis Tunstall, and Leandro von Werra.","author":"Lozhkov Anton","year":"2025","unstructured":"Anton Lozhkov, Hynek Kydl\u00ed\u010dek, Loubna Ben Allal, Guilherme Penedo, Edward Beeching, Quentin Gallou\u00e9dec, Nathan Habib, Lewis Tunstall, and Leandro von Werra. 2025. OpenR1-Math-220k. https:\/\/huggingface.co\/datasets\/open-r1\/OpenR1-Math-220k."},{"key":"e_1_3_2_1_36_1","unstructured":"OpenAI. 2024. Hello GPT-4o. https:\/\/openai.com\/index\/hello-gpt-4o\/ Accessed: 2026-02-06."},{"key":"e_1_3_2_1_37_1","unstructured":"OpenAI. 2025. gpt-oss-120b & gpt-oss-20b Model Card. arXiv:2508.10925 [cs.CL] https:\/\/arxiv.org\/arXiv:2508.10925"},{"key":"e_1_3_2_1_38_1","unstructured":"OpenAI. 2025. Introducing GPT-5.2. https:\/\/openai.com\/index\/introducing-gpt-5-2\/ Accessed: 2026-02-06."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Long Ouyang Jeffrey Wu Xu Jiang Diogo Almeida Carroll Wainwright Pamela Mishkin Chong Zhang Sandhini Agarwal Katarina Slama Alex Ray et al. 2022. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems.","DOI":"10.52202\/068431-2011"},{"key":"e_1_3_2_1_40_1","volume-title":"Nicolas Le Roux, and Alessandro Sordoni","author":"Pitis Silviu","year":"2024","unstructured":"Silviu Pitis, Ziang Xiao, Nicolas Le Roux, and Alessandro Sordoni. 2024. Improving Context-Aware Preference Modeling for Language Models. arXiv preprint, VarXiv:2407.14916 (2024)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","unstructured":"Rafael Rafailov Archit Sharma Eric Mitchell Christopher D Manning Stefano Ermon and Chelsea Finn. 2023. Direct preference optimization: Your language model is secretly a reward model. (2023).","DOI":"10.52202\/075280-2338"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.562"},{"key":"e_1_3_2_1_43_1","volume-title":"MetaSynth: Meta-Prompting-Driven Agentic Scaffolds for Diverse Synthetic Data Generation. arXiv preprint arXiv:2504.12563","author":"Riaz Haris","year":"2025","unstructured":"Haris Riaz, Sourav Bhabesh, Vinayak Arannil, Miguel Ballesteros, and Graham Horwood. 2025. MetaSynth: Meta-Prompting-Driven Agentic Scaffolds for Diverse Synthetic Data Generation. arXiv preprint arXiv:2504.12563 (2025)."},{"key":"e_1_3_2_1_44_1","volume-title":"Omar Zia Khan, and Abhinav Sethy","author":"Richardson Chris","year":"2023","unstructured":"Chris Richardson, Yao Zhang, Kellen Gillespie, Sudipta Kar, Arshdeep Singh, Zeynab Raeesy, Omar Zia Khan, and Abhinav Sethy. 2023. Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models. arXiv preprint, VarXiv:2310.20081 (2023)."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.399"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.555"},{"key":"e_1_3_2_1_47_1","unstructured":"ShareGPT. 2023. ShareGPT. https:\/\/sharegpt.com\/"},{"key":"e_1_3_2_1_48_1","volume-title":"VarXiv:2504.04365","author":"Spiess Claudio","year":"2025","unstructured":"Claudio Spiess, Mandana Vaziri, Louis Mandel, and Martin Hirzel. 2025. AutoPDL: Automatic Prompt Optimization for LLM Agents. arXiv preprint, VarXiv:2504.04365 (2025)."},{"key":"e_1_3_2_1_49_1","volume-title":"VarXiv:2506.04463","author":"Tan Zhaoxuan","year":"2025","unstructured":"Zhaoxuan Tan, Zheng Li, Tianyi Liu, Haodong Wang, Hyokun Yun, Ming Zeng, Pei Chen, Zhihan Zhang, Yifan Gao, Ruijie Wang, Priyanka Nigam, Bing Yin, and Menghan Jiang. 2025a. Aligning Large Language Models with Implicit Preferences from User-Generated Content. arXiv preprint, VarXiv:2506.04463 (2025)."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.emnlp-main.371"},{"key":"e_1_3_2_1_51_1","volume-title":"VarXiv:2510.16282","author":"Tan Zhaoxuan","year":"2025","unstructured":"Zhaoxuan Tan, Zixuan Zhang, Haoyang Wen, Zheng Li, Rongzhi Zhang, Pei Chen, Fengran Mo, Zheyuan Liu, Qingkai Zeng, Qingyu Yin, and Menghan Jiang. 2025b. Instant Personalized Large Language Model Adaptation via Hypernetwork. arXiv preprint, VarXiv:2510.16282 (2025)."},{"key":"e_1_3_2_1_52_1","volume-title":"Hashimoto","author":"Taori Rohan","year":"2023","unstructured":"Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. Stanford Alpaca: An Instruction-following LLaMA model. https:\/\/github.com\/tatsu-lab\/stanford_alpaca."},{"key":"e_1_3_2_1_53_1","unstructured":"Qwen Team. 2025. Qwen3 Technical Report. arXiv:2505.09388 [cs.CL] https:\/\/arxiv.org\/arXiv:2505.09388"},{"key":"e_1_3_2_1_54_1","first-page":"2522","volume-title":"Proceedings of the AAAI\/ACM Conference on AI, Ethics, and Society","volume":"8","author":"Tran Sarah","year":"2025","unstructured":"Sarah Tran, Hongfan Lu, Isaac Slaughter, Bernease Herman, Aayushi Dangol, Yue Fu, Lufei Chen, Biniyam Gebreyohannes, Bill Howe, Alexis Hiniker, et al., 2025. Understanding Privacy Norms Around LLM-Based Chatbots: A Contextual Integrity Perspective. In Proceedings of the AAAI\/ACM Conference on AI, Ethics, and Society, Vol. V8. 2522-2534."},{"key":"e_1_3_2_1_55_1","volume-title":"Yuchen Eleanor Jiang, and Wangchunshu Zhou","author":"Wang Tiannan","year":"2024","unstructured":"Tiannan Wang, Meiling Tao, Ruoyu Fang, Huilin Wang, Shuai Wang, Yuchen Eleanor Jiang, and Wangchunshu Zhou. 2024. AI PERSONA: Towards Life-long Personalization of LLMs. arXiv preprint, VarXiv:2412.13103 (2024)."},{"key":"e_1_3_2_1_56_1","volume-title":"AdaReasoner: Adaptive Reasoning Enables More Flexible Thinking. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=VjjJlJ5qik","author":"Wang Xiangqi","year":"2025","unstructured":"Xiangqi Wang, Yue Huang, Yanbo Wang, Xiaonan Luo, Kehan Guo, Yujun Zhou, and Xiangliang Zhang. 2025a. AdaReasoner: Adaptive Reasoning Enables More Flexible Thinking. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=VjjJlJ5qik"},{"key":"e_1_3_2_1_57_1","volume-title":"DyFlow: Dynamic Workflow Framework for Agentic Reasoning. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=0pbUfmwNTy","author":"Wang Yanbo","year":"2025","unstructured":"Yanbo Wang, Zixiang Xu, Yue Huang, Xiangqi Wang, Zirui Song, Lang Gao, Chenxi Wang, Xiangru Tang, Yue Zhao, Arman Cohan, Xiangliang Zhang, and Xiuying Chen. 2025b. DyFlow: Dynamic Workflow Framework for Agentic Reasoning. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=0pbUfmwNTy"},{"key":"e_1_3_2_1_58_1","volume-title":"Simple synthetic data reduces sycophancy in large language models. arXiv preprint arXiv:2308.03958","author":"Wei Jerry","year":"2023","unstructured":"Jerry Wei, Da Huang, Yifeng Lu, Denny Zhou, and Quoc V Le. 2023. Simple synthetic data reduces sycophancy in large language models. arXiv preprint arXiv:2308.03958 (2023)."},{"key":"e_1_3_2_1_59_1","volume-title":"VarXiv:2410.03642","author":"Wu Shujin","year":"2024","unstructured":"Shujin Wu, May Fung, Cheng Qian, Jeonghwan Kim, Dilek Hakkani-Tur, and Heng Ji. 2024. Aligning LLMs with Individual Preferences via Interaction. arXiv preprint, VarXiv:2410.03642 (2024)."},{"key":"e_1_3_2_1_60_1","volume-title":"International Conference on Machine Learning.","author":"Wu Shirley","year":"2025","unstructured":"Shirley Wu, Michel Galley, Baolin Peng, Hao Cheng, Gavin Li, Yao Dou, Weixin Cai, James Zou, Jure Leskovec, and Jianfeng Gao. 2025. Collabllm: From passive responders to active collaborators. In International Conference on Machine Learning."},{"key":"e_1_3_2_1_61_1","volume-title":"VarXiv:2502.06855","author":"Xiang Jinyu","year":"2025","unstructured":"Jinyu Xiang, Jiayi Zhang, Zhaoyang Yu, Fengwei Teng, Jinhao Tu, Xinbing Liang, Sirui Hong, Chenglin Wu, and Yuyu Luo. 2025. Self-Supervised Prompt Optimization. arXiv preprint, VarXiv:2502.06855 (2025)."},{"key":"e_1_3_2_1_62_1","volume-title":"Magpie: Alignment data synthesis from scratch by prompting aligned llms with nothing. arXiv preprint arXiv:2406.08464","author":"Xu Zhangchen","year":"2024","unstructured":"Zhangchen Xu, Fengqing Jiang, Luyao Niu, Yuntian Deng, Radha Poovendran, Yejin Choi, and Bill Yuchen Lin. 2024. Magpie: Alignment data synthesis from scratch by prompting aligned llms with nothing. arXiv preprint arXiv:2406.08464 (2024)."},{"key":"e_1_3_2_1_63_1","volume-title":"ICPL: Few-shot In-context Preference Learning via LLMs.","author":"Yu Chao","year":"2024","unstructured":"Chao Yu, Qixin Tan, Hong Lu, Jiaxuan Gao, Xinting Yang, Yu Wang, Yi Wu, and Eugene Vinitsky. 2024. ICPL: Few-shot In-context Preference Learning via LLMs."},{"key":"e_1_3_2_1_64_1","volume-title":"Modular Framework for Prompt Optimization. arXiv preprint, VarXiv:2512.02840","author":"Zehle Tom","year":"2025","unstructured":"Tom Zehle, Timo Heiss, Moritz Schlager, Matthias Assenmacher, and Matthias Feurer. 2025. promptolution: A Unified, Modular Framework for Prompt Optimization. arXiv preprint, VarXiv:2512.02840 (2025)."},{"key":"e_1_3_2_1_65_1","volume-title":"International Conference on Learning Representations (ICLR).","author":"Zeng Zhiyuan","year":"2024","unstructured":"Zhiyuan Zeng, Jiatong Yu, Tianyu Gao, Yu Meng, Tanya Goyal, and Danqi Chen. 2024. Evaluating Large Language Models at Evaluating Instruction Following. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_66_1","volume-title":"VarXiv:2410.10762","author":"Zhang Jiayi","year":"2024","unstructured":"Jiayi Zhang, Jinyu Xiang, Zhaoyang Yu, Fengwei Teng, Xionghui Chen, Jiaqi Chen, Mingchen Zhuge, Xin Cheng, Sirui Hong, Jinlin Wang, Bingnan Zheng, Bangbang Liu, Yuyu Luo, and Chenglin Wu. 2024b. AFlow: Automating Agentic Workflow Generation. arXiv preprint, VarXiv:2410.10762 (2024)."},{"key":"e_1_3_2_1_67_1","volume-title":"Oasis: One Image is All You Need for Multimodal Instruction Data Synthesis. arXiv preprint arXiv:2503.08741","author":"Zhang Letian","year":"2025","unstructured":"Letian Zhang, Quan Cui, Bingchen Zhao, and Cheng Yang. 2025b. Oasis: One Image is All You Need for Multimodal Instruction Data Synthesis. arXiv preprint arXiv:2503.08741 (2025)."},{"key":"e_1_3_2_1_68_1","volume-title":"PROPER: A Progressive Learning Framework for Personalized Large Language Models with Group-Level Adaptation. In Annual Meeting of the Association for Computational Linguistics.","author":"Zhang Linhai","year":"2025","unstructured":"Linhai Zhang, Jialong Wu, Deyu Zhou, and Yulan He. 2025c. PROPER: A Progressive Learning Framework for Personalized Large Language Models with Group-Level Adaptation. In Annual Meeting of the Association for Computational Linguistics."},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.findings-acl.260"},{"key":"e_1_3_2_1_70_1","volume-title":"VarXiv:2411.00027","author":"Zhang Zhehao","year":"2024","unstructured":"Zhehao Zhang, Ryan A. Rossi, Branislav Kveton, Yijia Shao, Diyi Yang, Hamed Zamani, Franck Dernoncourt, Joe Barrow, Tong Yu, Sungchul Kim, Ruiyi Zhang, Jiuxiang Gu, Tyler Derr, Hongjie Chen, Ju-Ying Wu, Xiang Chen, Zichao Wang, Subrata Mitra, Nedim Lipka, Nesreen K. Ahmed, and Yu Wang. 2024a. Personalization of Large Language Models: A Survey. arXiv preprint, VarXiv:2411.00027 (2024)."},{"key":"e_1_3_2_1_71_1","volume-title":"The Twelfth International Conference on Learning Representations.","author":"Zhao Wenting","unstructured":"Wenting Zhao, Xiang Ren, Jack Hessel, Claire Cardie, Yejin Choi, and Yuntian Deng. [n.d.]. WildChat: 1M ChatGPT Interaction Logs in the Wild. In The Twelfth International Conference on Learning Representations."},{"key":"e_1_3_2_1_72_1","volume-title":"VarXiv:2506.02368","author":"Zhao Xiaoyan","year":"2025","unstructured":"Xiaoyan Zhao, Juntao You, Yang Zhang, Wenjie Wang, Hong Cheng, Fuli Feng, See-Kiong Ng, and Tat-Seng Chua. 2025. NextQuill: Causal Preference Modeling for Enhancing LLM Personalization. arXiv preprint, VarXiv:2506.02368 (2025)."},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"crossref","unstructured":"Lianmin Zheng Wei-Lin Chiang Ying Sheng Siyuan Zhuang Zhanghao Wu Yonghao Zhuang Zi Lin Zhuohan Li Dacheng Li Eric Xing et al. 2023. Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in Neural Information Processing Systems V36 (2023) 46595-46623.","DOI":"10.52202\/075280-2020"},{"key":"e_1_3_2_1_74_1","volume-title":"Jie Fu, Wenhu Chen, and Xiang Yue.","author":"Zheng Tianyu","year":"2024","unstructured":"Tianyu Zheng, Ge Zhang, Tianhao Shen, Xueling Liu, Bill Yuchen Lin, Jie Fu, Wenhu Chen, and Xiang Yue. 2024. Opencodeinterpreter: Integrating code generation with execution and refinement. arXiv preprint arXiv:2402.14658 (2024)."},{"key":"e_1_3_2_1_75_1","volume-title":"Synthet2c: Generating synthetic data for fine-tuning large language models on the text2cypher task. arXiv preprint arXiv:2406.10710","author":"Zhong Ziije","year":"2024","unstructured":"Ziije Zhong, Linqing Zhong, Zhaoze Sun, Qingyun Jin, Zengchang Qin, and Xiaofan Zhang. 2024. Synthet2c: Generating synthetic data for fine-tuning large language models on the text2cypher task. arXiv preprint arXiv:2406.10710 (2024)."},{"key":"e_1_3_2_1_76_1","volume-title":"Instruction-following evaluation for large language models. arXiv preprint arXiv:2311.07911","author":"Zhou Jeffrey","year":"2023","unstructured":"Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou. 2023. Instruction-following evaluation for large language models. arXiv preprint arXiv:2311.07911 (2023)."},{"key":"e_1_3_2_1_77_1","volume-title":"HYDRA: Model Factorization Framework for Black-Box LLM Personalization. arXiv preprint, VarXiv:2406.02888","author":"Zhuang Yuchen","year":"2024","unstructured":"Yuchen Zhuang, Haotian Sun, Yue Yu, Rushi Qiang, Qifan Wang, Chao Zhang, and Bo Dai. 2024. HYDRA: Model Factorization Framework for Black-Box LLM Personalization. arXiv preprint, VarXiv:2406.02888 (2024)."},{"key":"e_1_3_2_1_78_1","volume-title":"Kulin","author":"Zhuravlev Viktor N.","year":"2025","unstructured":"Viktor N. Zhuravlev, Artur R. Khairullin, Ernest A. Dyagin, Alena N. Sitkina, and Nikita I. Kulin. 2025. Automatic Prompt Optimization with Prompt Distillation. arXiv preprint, VarXiv:2508.18992 (2025)."}],"event":{"name":"KDD '26: The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Jeju Island Republic of Korea","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"deposited":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T01:24:43Z","timestamp":1786065883000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3770855.3817563"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,8]]},"references-count":78,"alternative-id":["10.1145\/3770855.3817563","10.1145\/3770855"],"URL":"https:\/\/doi.org\/10.1145\/3770855.3817563","relation":{},"subject":[],"published":{"date-parts":[[2026,8,8]]},"assertion":[{"value":"2026-08-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}