{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:18:52Z","timestamp":1784179132335,"version":"3.55.0"},"reference-count":67,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:p>Display advertising plays a crucial role in benefiting advertisers, publishers, and users. Traditional display advertising systems employ a multi-stage architecture comprising retrieval, coarse ranking, ranking, and re-ranking. However, conventional retrieval methods primarily rely on ID-based learning-to-rank mechanisms, often underutilizing the content information of ads, like ads' title, and description. This limitation reduces the ability to generate diverse and relevant recommendation lists.<\/jats:p>\n          <jats:p>\n            To address this challenge, we propose leveraging the extensive world knowledge of large language models (LLMs). However, effectively integrating LLMs into advertising systems presents three key challenges: (\n            <jats:italic toggle=\"yes\">i) How to accurately capture user interests, (ii) How to bridge the knowledge gap between LLMs and advertising systems<\/jats:italic>\n            , and (\n            <jats:italic toggle=\"yes\">iii) How to efficiently deploy LLMs at scale.<\/jats:italic>\n            To overcome these challenges, we introduce\n            <jats:bold>LEADRE<\/jats:bold>\n            \u2014the\n            <jats:bold>L<\/jats:bold>\n            LM\n            <jats:bold>E<\/jats:bold>\n            mpowered Display\n            <jats:bold>AD<\/jats:bold>\n            vertisement\n            <jats:bold>RE<\/jats:bold>\n            commender system. LEADRE consists of three core components. The\n            <jats:bold>Intent-Aware Prompt Engineering<\/jats:bold>\n            module introduces multi-faceted knowledge and constructs intent-aware\n            <jats:italic toggle=\"yes\">&lt;Prompt, Response&gt;<\/jats:italic>\n            pairs, fine-tuning LLMs to generate ads tailored to users' personal interests. The\n            <jats:bold>Advertising-Specific Knowledge Alignment<\/jats:bold>\n            module incorporates auxiliary fine-tuning tasks and Direct Preference Optimization (DPO) to align LLMs with advertising semantics and business objectives. The\n            <jats:bold>Latency-Aware Model Deployment<\/jats:bold>\n            module integrates a hybrid service framework that balances latency-tolerant and latency-sensitive service, ensuring seamless online deployment.\n          <\/jats:p>\n          <jats:p>\n            Extensive offline experiments validate the effectiveness of LEADRE, demonstrating significant improvements across multiple evaluation metrics. Furthermore, online A\/B tests reveal a\n            <jats:bold>1.57%<\/jats:bold>\n            and\n            <jats:bold>1.17%<\/jats:bold>\n            increase in Gross Merchandise Value (GMV) for serviced users on WeChat Channels and Moments, respectively. LEADRE has been successfully deployed on both platforms, handling tens of billions of requests daily.\n          <\/jats:p>","DOI":"10.14778\/3750601.3750602","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:38:05Z","timestamp":1758029885000},"page":"4763-4776","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["LEADRE: Multi-Faceted Knowledge Enhanced LLM Empowered Display Advertisement Recommender System"],"prefix":"10.14778","volume":"18","author":[{"given":"Fengxin","family":"Li","sequence":"first","affiliation":[{"name":"Renmin University of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"Tencent Inc."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Liu","sequence":"additional","affiliation":[{"name":"Tencent Inc."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Zhou","sequence":"additional","affiliation":[{"name":"Tencent Inc."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Tencent Inc."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxiang","family":"Deng","sequence":"additional","affiliation":[{"name":"Tencent Inc."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Xue","sequence":"additional","affiliation":[{"name":"Tencent Inc."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dapeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Tencent Inc."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Xiao","sequence":"additional","affiliation":[{"name":"Tencent Inc."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haijie","family":"Gu","sequence":"additional","affiliation":[{"name":"Tencent Inc."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Jiang","sequence":"additional","affiliation":[{"name":"Tencent Inc."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyan","family":"Liu","sequence":"additional","affiliation":[{"name":"Tsinghua University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Biao","family":"Qin","sequence":"additional","affiliation":[{"name":"Renmin University of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"He","sequence":"additional","affiliation":[{"name":"Renmin University of China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3661383"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3608857"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403344"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462968"},{"key":"e_1_2_1_5_1","volume-title":"A Unified Framework for Campaign Performance Forecasting in Online Display Advertising. arXiv preprint arXiv:2202.11877","author":"Chen Jun","year":"2022","unstructured":"Jun Chen, Cheng Chen, Huayue Zhang, and Qing Tan. 2022. A Unified Framework for Campaign Performance Forecasting in Online Display Advertising. arXiv preprint arXiv:2202.11877 (2022)."},{"key":"e_1_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Jianlv Chen Shitao Xiao Peitian Zhang Kun Luo Defu Lian and Zheng Liu. 2024. BGE M3-Embedding: Multi-Lingual Multi-Functionality Multi-Granularity Text Embeddings Through Self-Knowledge Distillation. arXiv:2402.03216","DOI":"10.18653\/v1\/2024.findings-acl.137"},{"key":"e_1_2_1_7_1","volume-title":"Electra: Pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555","author":"Clark K","year":"2020","unstructured":"K Clark. 2020. Electra: Pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555 (2020)."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2020.2964552"},{"key":"e_1_2_1_9_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403113"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3523227.3546767"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41019-019-00115-y"},{"key":"e_1_2_1_13_1","volume-title":"International Conference on Machine Learning. PMLR, 12098\u201312107","author":"Guo Daya","year":"2023","unstructured":"Daya Guo, Canwen Xu, Nan Duan, Jian Yin, and Julian McAuley. 2023. Longcoder: A long-range pre-trained language model for code completion. In International Conference on Machine Learning. PMLR, 12098\u201312107."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3610639"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614949"},{"key":"e_1_2_1_16_1","volume-title":"Lexically constrained decoding for sequence generation using grid beam search. arXiv preprint arXiv:1704.07138","author":"Hokamp Chris","year":"2017","unstructured":"Chris Hokamp and Qun Liu. 2017. Lexically constrained decoding for sequence generation using grid beam search. arXiv preprint arXiv:1704.07138 (2017)."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1090"},{"key":"e_1_2_1_18_1","volume-title":"Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, and Jiawei Han.","author":"Huang Jiaxin","year":"2022","unstructured":"Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, and Jiawei Han. 2022. Large language models can self-improve. arXiv preprint arXiv:2210.11610 (2022)."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403305"},{"key":"e_1_2_1_20_1","volume-title":"Neural Graph Matching for Video Retrieval in Large-Scale Video-driven E-commerce. arXiv preprint arXiv:2408.00346","author":"Ji Houye","year":"2024","unstructured":"Houye Ji, Ye Tang, Zhaoxin Chen, Lixi Deng, Jun Hu, and Lei Su. 2024. Neural Graph Matching for Video Retrieval in Large-Scale Video-driven E-commerce. arXiv preprint arXiv:2408.00346 (2024)."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00035"},{"key":"e_1_2_1_22_1","volume-title":"An empirical survey on long document summarization: Datasets, models, and metrics. ACM computing surveys 55, 8","author":"Koh Huan Yee","year":"2022","unstructured":"Huan Yee Koh, Jiaxin Ju, Ming Liu, and Shirui Pan. 2022. An empirical survey on long document summarization: Datasets, models, and metrics. ACM computing surveys 55, 8 (2022), 1\u201335."},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330895"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01123"},{"key":"e_1_2_1_25_1","unstructured":"Jin Li Jie Liu Shangzhou Li Yao Xu Ran Cao Qi Li Biye Jiang Guan Wang Han Zhu Kun Gai et al. 2021. Truncation-Free Matching System for Display Advertising at Alibaba. arXiv preprint arXiv:2102.09283 (2021)."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599519"},{"key":"e_1_2_1_27_1","volume-title":"Planning First","author":"Li Kunze","year":"2024","unstructured":"Kunze Li and Yu Zhang. 2024. Planning First, Question Second: An LLM-Guided Method for Controllable Question Generation. In Findings of the Association for Computational Linguistics ACL 2024. 4715\u20134729."},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671884"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16549"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0135"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3597503.3639187"},{"key":"e_1_2_1_32_1","volume-title":"Fast lexically constrained decoding with dynamic beam allocation for neural machine translation. arXiv preprint arXiv:1804.06609","author":"Post Matt","year":"2018","unstructured":"Matt Post and David Vilar. 2018. Fast lexically constrained decoding with dynamic beam allocation for neural machine translation. arXiv preprint arXiv:1804.06609 (2018)."},{"key":"e_1_2_1_33_1","volume-title":"Learning how to ask: Querying LMs with mixtures of soft prompts. arXiv preprint arXiv:2104.06599","author":"Qin Guanghui","year":"2021","unstructured":"Guanghui Qin and Jason Eisner. 2021. Learning how to ask: Querying LMs with mixtures of soft prompts. arXiv preprint arXiv:2104.06599 (2021)."},{"key":"e_1_2_1_34_1","volume-title":"International conference on machine learning. PMLR, 8748\u20138763","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning. PMLR, 8748\u20138763."},{"key":"e_1_2_1_35_1","volume-title":"Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems 36","author":"Rafailov Rafael","year":"2024","unstructured":"Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2024. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems 36 (2024)."},{"key":"e_1_2_1_36_1","first-page":"1","article-title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","volume":"21","author":"Raffel Colin","year":"2020","unstructured":"Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. 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","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_1_37_1","volume-title":"Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Tran, Jonah Samost, et al.","author":"Rajput Shashank","year":"2024","unstructured":"Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan Hulikal Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Tran, Jonah Samost, et al. 2024. Recommender systems with generative retrieval. Advances in Neural Information Processing Systems 36 (2024)."},{"key":"e_1_2_1_38_1","volume-title":"Sentence-BERT: Sentence Embeddings using Siamese BERTNetworks. arXiv preprint arXiv:1908.10084","author":"Reimers N","year":"2019","unstructured":"N Reimers. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERTNetworks. arXiv preprint arXiv:1908.10084 (2019)."},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645458"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10796-022-10314-0"},{"key":"e_1_2_1_41_1","volume-title":"Multi-scenario and multi-task aware feature interaction for recommendation system. ACM Transactions on Knowledge Discovery from Data 18, 6","author":"Song Derun","year":"2024","unstructured":"Derun Song, Enneng Yang, Guibing Guo, Li Shen, Linying Jiang, and Xingwei Wang. 2024. Multi-scenario and multi-task aware feature interaction for recommendation system. ACM Transactions on Knowledge Discovery from Data 18, 6 (2024), 1\u201320."},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357895"},{"key":"e_1_2_1_43_1","unstructured":"Xingwu Sun Yanfeng Chen Yiqing Huang Ruobing Xie Jiaqi Zhu Kai Zhang Shuaipeng Li Zhen Yang Jonny Han Xiaobo Shu et al. 2024. Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent. arXiv:2411.02265"},{"key":"e_1_2_1_44_1","volume-title":"Spot: Better frozen model adaptation through soft prompt transfer. arXiv preprint arXiv:2110.07904","author":"Vu Tu","year":"2021","unstructured":"Tu Vu, Brian Lester, Noah Constant, Rami Al-Rfou, and Daniel Cer. 2021. Spot: Better frozen model adaptation through soft prompt transfer. arXiv preprint arXiv:2110.07904 (2021)."},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3544548.3580895"},{"key":"e_1_2_1_46_1","volume-title":"Text embeddings by weakly-supervised contrastive pre-training. arXiv preprint arXiv:2212.03533","author":"Wang Liang","year":"2022","unstructured":"Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022. Text embeddings by weakly-supervised contrastive pre-training. arXiv preprint arXiv:2212.03533 (2022)."},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3616855.3635853"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475665"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412258"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-024-01291-2"},{"key":"e_1_2_1_51_1","volume-title":"Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining.","author":"Xie Ruobing","year":"2022","unstructured":"Ruobing Xie, Qi Liu, Liangdong Wang, Shukai Liu, Bo Zhang, and Leyu Lin. 2022. Contrastive cross-domain recommendation in matching. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining."},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531963"},{"key":"e_1_2_1_53_1","volume-title":"Zhiming Cui, Xiaofang Zhou, and Hui Xiong.","author":"Xu Chengfeng","year":"2019","unstructured":"Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Jiajie Xu, Victor S Sheng S. Sheng, Zhiming Cui, Xiaofang Zhou, and Hui Xiong. 2019. Recurrent convolutional neural network for sequential recommendation. In The world wide web conference. 3398\u20133404."},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICISCE48695.2019.00031"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358113"},{"key":"e_1_2_1_56_1","volume-title":"XLNet: Generalized Autoregressive Pretraining for Language Understanding. arXiv preprint arXiv:1906.08237","author":"Yang Zhilin","year":"2019","unstructured":"Zhilin Yang. 2019. XLNet: Generalized Autoregressive Pretraining for Language Understanding. arXiv preprint arXiv:1906.08237 (2019)."},{"key":"e_1_2_1_57_1","volume-title":"A survey on large language model (llm) security and privacy: The good, the bad, and the ugly. High-Confidence Computing","author":"Yao Yifan","year":"2024","unstructured":"Yifan Yao, Jinhao Duan, Kaidi Xu, Yuanfang Cai, Zhibo Sun, and Yue Zhang. 2024. A survey on large language model (llm) security and privacy: The good, the bad, and the ugly. High-Confidence Computing (2024), 100211."},{"key":"e_1_2_1_58_1","volume-title":"RRHF: Rank responses to align language models with human feedback. Advances in Neural Information Processing Systems 36","author":"Yuan Hongyi","year":"2024","unstructured":"Hongyi Yuan, Zheng Yuan, Chuanqi Tan, Wei Wang, Songfang Huang, and Fei Huang. 2024. RRHF: Rank responses to align language models with human feedback. Advances in Neural Information Processing Systems 36 (2024)."},{"key":"e_1_2_1_59_1","volume-title":"Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. 1687\u20131691","author":"Yuan Xu","year":"2021","unstructured":"Xu Yuan, Dongsheng Duan, Lingling Tong, Lei Shi, and Cheng Zhang. 2021. Icaisr: Item categorical attribute integrated sequential recommendation. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. 1687\u20131691."},{"key":"e_1_2_1_60_1","volume-title":"Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval.","author":"Zhai ChengXiang","year":"2024","unstructured":"ChengXiang Zhai. 2024. Large language models and future of information retrieval: Opportunities and challenges. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval."},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3608860"},{"key":"e_1_2_1_62_1","volume-title":"Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING","author":"Zhang Jing","year":"2024","unstructured":"Jing Zhang, Hui Gao, Peng Zhang, Boda Feng, Wenmin Deng, and Yuexian Hou. 2024. LA-UCL: LLM-augmented unsupervised contrastive learning framework for few-shot text classification. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). 10198\u201310207."},{"key":"e_1_2_1_63_1","first-page":"295","article-title":"LLM-Cloud Complete: Leveraging cloud computing for efficient large language model-based code completion","volume":"3006","author":"Zhang Mingxuan","year":"2024","unstructured":"Mingxuan Zhang, Bo Yuan, Hanzhe Li, and Kangming Xu. 2024. LLM-Cloud Complete: Leveraging cloud computing for efficient large language model-based code completion. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023 5, 1 (2024), 295\u2013326.","journal-title":"Journal of Artificial Intelligence General science (JAIGS) ISSN"},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00632"},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00118"},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512111"},{"key":"e_1_2_1_67_1","volume-title":"Large language models for information retrieval: A survey. arXiv preprint arXiv:2308.07107","author":"Zhu Yutao","year":"2023","unstructured":"Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Haonan Chen, Zhicheng Dou, and Ji-Rong Wen. 2023. Large language models for information retrieval: A survey. arXiv preprint arXiv:2308.07107 (2023)."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3750601.3750602","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:43:50Z","timestamp":1758030230000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3750601.3750602"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8]]},"references-count":67,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["10.14778\/3750601.3750602"],"URL":"https:\/\/doi.org\/10.14778\/3750601.3750602","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2025,8]]},"assertion":[{"value":"2025-09-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}