{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T14:55:44Z","timestamp":1767970544566,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":71,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,6,20]]},"DOI":"10.1145\/3755881.3755889","type":"proceedings-article","created":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T11:46:17Z","timestamp":1761565577000},"page":"413-425","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["AUCAD: Automated Construction of Alignment Dataset from Log-Related Issues for Enhancing LLM-based Log Generation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6205-3593","authenticated-orcid":false,"given":"Hao","family":"Zhang","sequence":"first","affiliation":[{"name":"Software Institute, Nanjing University, Nanjing, Jiangsu, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3088-6213","authenticated-orcid":false,"given":"Dongjun","family":"Yu","sequence":"additional","affiliation":[{"name":"Software Institute, Nanjing University, Nanjing, Jiangsu, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3544-8636","authenticated-orcid":false,"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Software Institute, Nanjing University, Nanjing, Jiangsu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4576-0524","authenticated-orcid":false,"given":"Guoping","family":"Rong","sequence":"additional","affiliation":[{"name":"Software Institute, Nanjing University, Nanjing, Jiangsu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6713-2364","authenticated-orcid":false,"given":"Yongda","family":"Yu","sequence":"additional","affiliation":[{"name":"Software Institute, Nanjing University, Nanjing, Jiangsu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8221-981X","authenticated-orcid":false,"given":"Haifeng","family":"Shen","sequence":"additional","affiliation":[{"name":"Faculty of Science and Engineering, Southern Cross University, Bilinga, Queensland, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9159-5331","authenticated-orcid":false,"given":"He","family":"Zhang","sequence":"additional","affiliation":[{"name":"Software Institute, Nanjing University, Nanjing, Jiangsu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6500-0341","authenticated-orcid":false,"given":"Dong","family":"Shao","sequence":"additional","affiliation":[{"name":"Software Institute, Nanjing University, Nanjing, Jiangsu, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8702-2826","authenticated-orcid":false,"given":"Hongyu","family":"Kuang","sequence":"additional","affiliation":[{"name":"Software Institute, Nanjing University, Nanjing, Jiangsu, China"}]}],"member":"320","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"key":"e_1_3_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/2021.NAACL-MAIN.211"},{"key":"e_1_3_3_3_3_2","unstructured":"Toufique Ahmed Kunal\u00a0Suresh Pai Premkumar Devanbu and Earl\u00a0T. Barr. 2024. Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization). arxiv:https:\/\/arXiv.org\/abs\/2304.06815\u00a0[cs.SE] https:\/\/arxiv.org\/abs\/2304.06815"},{"key":"e_1_3_3_3_4_2","unstructured":"AI@Meta. 2024. Llama 3.1 Model Card. https:\/\/github.com\/meta-llama\/llama-models\/blob\/main\/models\/llama3_1\/MODEL_CARD.md"},{"key":"e_1_3_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3649329.3657356"},{"key":"e_1_3_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE-COMPANION.2019.00080"},{"key":"e_1_3_3_3_7_2","doi-asserted-by":"publisher","unstructured":"Boyuan Chen and Zhen\u00a0Ming Jiang. 2019. Extracting and studying the Logging-Code-Issue-Introducing changes in Java-based large-scale open source software systems. Empirical Software Engineering 24 4 (2019) 2285\u20132322. 10.1007\/S10664-019-09690-0","DOI":"10.1007\/S10664-019-09690-0"},{"key":"e_1_3_3_3_8_2","doi-asserted-by":"publisher","unstructured":"Boyuan Chen and Zhen Ming\u00a0(Jack) Jiang. 2021. A Survey of Software Log Instrumentation. ACM Comput. Surv. 54 4 Article 90 (may 2021) 34\u00a0pages. 10.1145\/3448976","DOI":"10.1145\/3448976"},{"key":"e_1_3_3_3_9_2","unstructured":"Xiangning Chen Chen Liang Da Huang Esteban Real Kaiyuan Wang Yao Liu Hieu Pham Xuanyi Dong Thang Luong Cho-Jui Hsieh Yifeng Lu and Quoc\u00a0V. Le. 2023. Symbolic Discovery of Optimization Algorithms. arxiv:https:\/\/arXiv.org\/abs\/2302.06675\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2302.06675"},{"key":"e_1_3_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.5555\/3294996.3295184"},{"key":"e_1_3_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3379597.3387487"},{"key":"e_1_3_3_3_12_2","doi-asserted-by":"crossref","unstructured":"Jacob Cohen. 1960. A coefficient of agreement for nominal scales. Educational and psychological measurement 20 1 (1960) 37\u201346.","DOI":"10.1177\/001316446002000104"},{"key":"e_1_3_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/SANER53432.2022.00051"},{"key":"e_1_3_3_3_14_2","doi-asserted-by":"publisher","unstructured":"Mikhail Evtikhiev Egor Bogomolov Yaroslav Sokolov and Timofey Bryksin. 2023. Out of the BLEU: How should we assess quality of the Code Generation models? J. Syst. Softw. 203 C (sep 2023) 17\u00a0pages. 10.1016\/j.jss.2023.111741","DOI":"10.1016\/j.jss.2023.111741"},{"key":"e_1_3_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE-FoSE59343.2023.00008"},{"key":"e_1_3_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/2591062.2591175"},{"key":"e_1_3_3_3_17_2","doi-asserted-by":"publisher","unstructured":"Ying Fu Meng Yan Pinjia He Chao Liu Xiaohong Zhang and Dan Yang. 2024. End-to-end log statement generation at block-level. Journal of Systems and Software 216 (2024) 112146. 10.1016\/j.jss.2024.112146","DOI":"10.1016\/j.jss.2024.112146"},{"key":"e_1_3_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-6998-5_12"},{"key":"e_1_3_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3597503.3608134"},{"key":"e_1_3_3_3_20_2","series-title":"Proceedings of Machine Learning Research","first-page":"4447","volume-title":"Proceedings of The 27th International Conference on Artificial Intelligence and Statistics","volume":"238","author":"Gheshlaghi\u00a0Azar Mohammad","year":"2024","unstructured":"Mohammad Gheshlaghi\u00a0Azar, Zhaohan Daniel\u00a0Guo, Bilal Piot, Remi Munos, Mark Rowland, Michal Valko, and Daniele Calandriello. 2024. A General Theoretical Paradigm to Understand Learning from Human Preferences. In Proceedings of The 27th International Conference on Artificial Intelligence and Statistics(Proceedings of Machine Learning Research, Vol.\u00a0238). PMLR, 4447\u20134455. https:\/\/proceedings.mlr.press\/v238\/gheshlaghi-azar24a.html"},{"key":"e_1_3_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASE51524.2021.9678596"},{"key":"e_1_3_3_3_22_2","doi-asserted-by":"publisher","unstructured":"Shenghui Gu Guoping Rong Tian Ren He Zhang Haifeng Shen Yongda Yu Xian Li Jian Ouyang and Chunan Chen. 2023. TrinityRCL: Multi-Granular and Code-Level Root Cause Localization Using Multiple Types of Telemetry Data in Microservice Systems. IEEE Transactions on Software Engineering 49 5 (2023) 3071\u20133088. 10.1109\/TSE.2023.3241299","DOI":"10.1109\/TSE.2023.3241299"},{"key":"e_1_3_3_3_23_2","doi-asserted-by":"publisher","unstructured":"Shenghui Gu Guoping Rong He Zhang and Haifeng Shen. 2023. Logging Practices in Software Engineering: A Systematic Mapping Study. IEEE Transactions on Software Engineering 49 2 (2023) 902\u2013923. 10.1109\/TSE.2022.3166924","DOI":"10.1109\/TSE.2022.3166924"},{"key":"e_1_3_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1609\/AAAI.V38I1.27764"},{"key":"e_1_3_3_3_25_2","doi-asserted-by":"publisher","unstructured":"Mehran Hassani Weiyi Shang Emad Shihab and Nikolaos Tsantalis. 2018. Studying and detecting log-related issues. Empirical Softw. Engg. 23 6 (dec 2018) 3248\u20133280. 10.1007\/s10664-018-9603-z","DOI":"10.1007\/s10664-018-9603-z"},{"key":"e_1_3_3_3_26_2","unstructured":"Edward\u00a0J. Hu Yelong Shen Phillip Wallis Zeyuan Allen-Zhu Yuanzhi Li Shean Wang Lu Wang and Weizhu Chen. 2021. LoRA: Low-Rank Adaptation of Large Language Models. arxiv:https:\/\/arXiv.org\/abs\/2106.09685\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2106.09685"},{"key":"e_1_3_3_3_27_2","unstructured":"Yuan Huang Yinan Chen Xiangping Chen Junqi Chen Rui Peng Zhicao Tang Jinbo Huang Furen Xu and Zibin Zheng. 2024. Generative Software Engineering. arxiv:https:\/\/arXiv.org\/abs\/2403.02583\u00a0[cs.SE] https:\/\/arxiv.org\/abs\/2403.02583"},{"key":"e_1_3_3_3_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/SANER.2016.29"},{"key":"e_1_3_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3629526.3645033"},{"key":"e_1_3_3_3_30_2","unstructured":"Blagovesta Kostova Seda G\u00fcrses and Carmela Troncoso. 2020. Privacy Engineering Meets Software Engineering. On the Challenges of Engineering Privacy ByDesign. arxiv:https:\/\/arXiv.org\/abs\/2007.08613\u00a0[cs.SE] https:\/\/arxiv.org\/abs\/2007.08613"},{"key":"e_1_3_3_3_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASE51524.2021.9678773"},{"key":"e_1_3_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510155"},{"key":"e_1_3_3_3_33_2","doi-asserted-by":"publisher","unstructured":"Yichen Li Yintong Huo Zhihan Jiang Renyi Zhong Pinjia He Yuxin Su Lionel\u00a0C. Briand and Michael\u00a0R. Lyu. 2024. Exploring the Effectiveness of LLMs in Automated Logging Statement Generation: An Empirical Study. IEEE Transactions on Software Engineering 50 12 (2024) 3188\u20133207. 10.1109\/TSE.2024.3475375","DOI":"10.1109\/TSE.2024.3475375"},{"key":"e_1_3_3_3_34_2","doi-asserted-by":"publisher","unstructured":"Yichen Li Yintong Huo Renyi Zhong Zhihan Jiang Jinyang Liu Junjie Huang Jiazhen Gu Pinjia He and Michael\u00a0R. Lyu. 2024. Go Static: Contextualized Logging Statement Generation. Proc. ACM Softw. Eng. 1 FSE Article 28 (jul 2024) 22\u00a0pages. 10.1145\/3643754","DOI":"10.1145\/3643754"},{"key":"e_1_3_3_3_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00131"},{"key":"e_1_3_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3639478.3643062"},{"key":"e_1_3_3_3_37_2","unstructured":"Xinyu Lin Wenjie Wang Yongqi Li Shuo Yang Fuli Feng Yinwei Wei and Tat-Seng Chua. 2024. Data-efficient Fine-tuning for LLM-based Recommendation. arxiv:https:\/\/arXiv.org\/abs\/2401.17197\u00a0[cs.IR] https:\/\/arxiv.org\/abs\/2401.17197"},{"key":"e_1_3_3_3_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3533767.3534379"},{"key":"e_1_3_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3639478.3643108"},{"key":"e_1_3_3_3_40_2","doi-asserted-by":"publisher","unstructured":"Zhongxin Liu Xin Xia David Lo Zhenchang Xing Ahmed\u00a0E. Hassan and Shanping Li. 2021. Which Variables Should I Log? IEEE Transactions on Software Engineering 47 9 (2021) 2012\u20132031. 10.1109\/TSE.2019.2941943","DOI":"10.1109\/TSE.2019.2941943"},{"key":"e_1_3_3_3_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE59848.2023.00026"},{"key":"e_1_3_3_3_42_2","doi-asserted-by":"publisher","unstructured":"Antonio Mastropaolo Valentina Ferrari Luca Pascarella and Gabriele Bavota. 2024. Log statements generation via deep learning: Widening the support provided to developers. Journal of Systems and Software 210 (2024) 111947. 10.1016\/j.jss.2023.111947","DOI":"10.1016\/j.jss.2023.111947"},{"key":"e_1_3_3_3_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3511561"},{"key":"e_1_3_3_3_44_2","first-page":"124198","volume-title":"Advances in Neural Information Processing Systems","volume":"37","author":"Meng Yu","year":"2024","unstructured":"Yu Meng, Mengzhou Xia, and Danqi Chen. 2024. SimPO: Simple Preference Optimization with a Reference-Free Reward. In Advances in Neural Information Processing Systems, Vol.\u00a037. Curran Associates, Inc., 124198\u2013124235. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2024\/file\/e099c1c9699814af0be873a175361713-Paper-Conference.pdf"},{"key":"e_1_3_3_3_45_2","unstructured":"Eric Mitchell. 2023. A note on DPO with noisy preferences & relationship to IPO. https:\/\/ericmitchell.ai\/cdpo.pdf"},{"key":"e_1_3_3_3_46_2","doi-asserted-by":"publisher","unstructured":"Arghavan Moradi\u00a0Dakhel Vahid Majdinasab Amin Nikanjam Foutse Khomh Michel\u00a0C. Desmarais and Zhen Ming\u00a0(Jack) Jiang. 2023. GitHub Copilot AI pair programmer: Asset or Liability? J. Syst. Softw. 203 C (sep 2023) 23\u00a0pages. 10.1016\/j.jss.2023.111734","DOI":"10.1016\/j.jss.2023.111734"},{"key":"e_1_3_3_3_47_2","doi-asserted-by":"publisher","DOI":"10.3115\/1073083.1073135"},{"key":"e_1_3_3_3_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2015.145"},{"key":"e_1_3_3_3_49_2","first-page":"53728","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Rafailov Rafael","year":"2023","unstructured":"Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher\u00a0D Manning, Stefano Ermon, and Chelsea Finn. 2023. Direct Preference Optimization: Your Language Model is Secretly a Reward Model. In Advances in Neural Information Processing Systems, Vol.\u00a036. Curran Associates, Inc., 53728\u201353741. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2023\/file\/a85b405ed65c6477a4fe8302b5e06ce7-Paper-Conference.pdf"},{"key":"e_1_3_3_3_50_2","unstructured":"Colin Raffel Noam Shazeer Adam Roberts Katherine Lee Sharan Narang Michael Matena Yanqi Zhou Wei Li and Peter\u00a0J. Liu. 2020. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Journal of Machine Learning Research 21 140 (2020) 1\u201367. http:\/\/jmlr.org\/papers\/v21\/20-074.html"},{"key":"e_1_3_3_3_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSME46990.2020.00012"},{"key":"e_1_3_3_3_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510060"},{"key":"e_1_3_3_3_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE59848.2023.00083"},{"key":"e_1_3_3_3_54_2","unstructured":"Weisong Sun Chunrong Fang Yudu You Yun Miao Yi Liu Yuekang Li Gelei Deng Shenghan Huang Yuchen Chen Quanjun Zhang Hanwei Qian Yang Liu and Zhenyu Chen. 2023. Automatic Code Summarization via ChatGPT: How Far Are We? arxiv:https:\/\/arXiv.org\/abs\/2305.12865\u00a0[cs.SE] https:\/\/arxiv.org\/abs\/2305.12865"},{"key":"e_1_3_3_3_55_2","unstructured":"Fahim Tajwar Anikait Singh Archit Sharma Rafael Rafailov Jeff Schneider Tengyang Xie Stefano Ermon Chelsea Finn and Aviral Kumar. 2024. Preference Fine-Tuning of LLMs Should Leverage Suboptimal On-Policy Data. arxiv:https:\/\/arXiv.org\/abs\/2404.14367\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2404.14367"},{"key":"e_1_3_3_3_56_2","doi-asserted-by":"publisher","unstructured":"Junjie Wang Yuchao Huang Chunyang Chen Zhe Liu Song Wang and Qing Wang. 2024. Software Testing With Large Language Models: Survey Landscape and Vision. IEEE Transactions on Software Engineering 50 4 (2024) 911\u2013936. 10.1109\/TSE.2024.3368208","DOI":"10.1109\/TSE.2024.3368208"},{"key":"e_1_3_3_3_57_2","unstructured":"Jason Wei Maarten Bosma Vincent\u00a0Y. Zhao Kelvin Guu Adams\u00a0Wei Yu Brian Lester Nan Du Andrew\u00a0M. Dai and Quoc\u00a0V. Le. 2022. Finetuned Language Models Are Zero-Shot Learners. arxiv:https:\/\/arXiv.org\/abs\/2109.01652\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2109.01652"},{"key":"e_1_3_3_3_58_2","unstructured":"Yuxiang Wei Zhe Wang Jiawei Liu Yifeng Ding and Lingming Zhang. 2024. Magicoder: Empowering Code Generation with OSS-Instruct. arxiv:https:\/\/arXiv.org\/abs\/2312.02120\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2312.02120"},{"key":"e_1_3_3_3_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/3545945.3569830"},{"key":"e_1_3_3_3_60_2","unstructured":"Xiaoyuan Xie Zhipeng Cai Songqiang Chen and Jifeng Xuan. 2024. FastLog: An End-to-End Method to Efficiently Generate and Insert Logging Statements. arxiv:https:\/\/arXiv.org\/abs\/2311.02862\u00a0[cs.SE] https:\/\/arxiv.org\/abs\/2311.02862"},{"key":"e_1_3_3_3_61_2","unstructured":"Zhenchang Xing Qing Huang Yu Cheng Liming Zhu Qinghua Lu and Xiwei Xu. 2023. Prompt Sapper: LLM-Empowered Software Engineering Infrastructure for AI-Native Services. arxiv:https:\/\/arXiv.org\/abs\/2306.02230\u00a0[cs.SE] https:\/\/arxiv.org\/abs\/2306.02230"},{"key":"e_1_3_3_3_62_2","doi-asserted-by":"publisher","DOI":"10.1145\/3597503.3623326"},{"key":"e_1_3_3_3_63_2","doi-asserted-by":"publisher","unstructured":"Jingfeng Yang Hongye Jin Ruixiang Tang Xiaotian Han Qizhang Feng Haoming Jiang Shaochen Zhong Bing Yin and Xia Hu. 2024. Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond. ACM Trans. Knowl. Discov. Data 18 6 Article 160 (apr 2024) 32\u00a0pages. 10.1145\/3649506","DOI":"10.1145\/3649506"},{"key":"e_1_3_3_3_64_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28777"},{"key":"e_1_3_3_3_65_2","unstructured":"Yongcheng Zeng Guoqing Liu Weiyu Ma Ning Yang Haifeng Zhang and Jun Wang. 2024. Token-level Direct Preference Optimization. arxiv:https:\/\/arXiv.org\/abs\/2404.11999\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2404.11999"},{"key":"e_1_3_3_3_66_2","doi-asserted-by":"publisher","unstructured":"Haonan Zhang Yiming Tang Maxime Lamothe Heng Li and Weiyi Shang. 2022. Studying logging practice in test code. Empirical Softw. Engg. 27 4 (jul 2022) 45\u00a0pages. 10.1007\/s10664-022-10139-0","DOI":"10.1007\/s10664-022-10139-0"},{"key":"e_1_3_3_3_67_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE-Companion58688.2023.00031"},{"key":"e_1_3_3_3_68_2","unstructured":"Ziyin Zhang Chaoyu Chen Bingchang Liu Cong Liao Zi Gong Hang Yu Jianguo Li and Rui Wang. 2024. Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code. arxiv:https:\/\/arXiv.org\/abs\/2311.07989\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2311.07989"},{"key":"e_1_3_3_3_69_2","unstructured":"Wayne\u00a0Xin Zhao Kun Zhou Junyi Li Tianyi Tang Xiaolei Wang Yupeng Hou Yingqian Min Beichen Zhang Junjie Zhang Zican Dong Yifan Du Chen Yang Yushuo Chen Zhipeng Chen Jinhao Jiang Ruiyang Ren Yifan Li Xinyu Tang Zikang Liu Peiyu Liu Jian-Yun Nie and Ji-Rong Wen. 2025. A Survey of Large Language Models. arxiv:https:\/\/arXiv.org\/abs\/2303.18223\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2303.18223"},{"key":"e_1_3_3_3_70_2","unstructured":"Yao Zhao Rishabh Joshi Tianqi Liu Misha Khalman Mohammad Saleh and Peter\u00a0J. Liu. 2023. SLiC-HF: Sequence Likelihood Calibration with Human Feedback. arxiv:https:\/\/arXiv.org\/abs\/2305.10425\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2305.10425"},{"key":"e_1_3_3_3_71_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2015.60"},{"key":"e_1_3_3_3_72_2","doi-asserted-by":"publisher","DOI":"10.1145\/3361242.3361261"}],"event":{"name":"Internetware 2025: the 16th International Conference on Internetware","location":"Trondheim Norway","acronym":"Internetware 2025","sponsor":["SIGSOFT ACM Special Interest Group on Artificial Intelligence"]},"container-title":["Proceedings of the 16th International Conference on Internetware"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3755881.3755889","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T11:47:40Z","timestamp":1761565660000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3755881.3755889"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,20]]},"references-count":71,"alternative-id":["10.1145\/3755881.3755889","10.1145\/3755881"],"URL":"https:\/\/doi.org\/10.1145\/3755881.3755889","relation":{},"subject":[],"published":{"date-parts":[[2025,6,20]]},"assertion":[{"value":"2025-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}