{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:15:34Z","timestamp":1784178934609,"version":"3.55.0"},"reference-count":60,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62272467"],"award-info":[{"award-number":["62272467"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE"},{"name":"Beijing Key Laboratory of Big Data Management and Analysis Methods"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2024,11,30]]},"abstract":"<jats:p>In recent years, various dense retrieval methods have been developed to improve the performance of search engines with a vectorized index. However, these approaches require a large pre-computed index and have a limited capacity to memorize all semantics in a document within a single vector. To address these issues, researchers have explored end-to-end generative retrieval models that use a seq-to-seq generative model to directly return identifiers of relevant documents. Although these models have been effective, they are often trained with the MLE method. It only encourages the model to assign a high probability to the relevant document identifier, ignoring the relevance comparisons of other documents. This may lead to performance degradation in ranking tasks, where the core is to compare the relevance between documents. To address this issue, we propose a ranking-oriented generative retrieval model that incorporates relevance signals to better estimate the relative relevance of different documents in ranking tasks. Based upon the analysis of the optimization objectives of dense retrieval and generative retrieval, we propose utilizing dense retrieval to provide relevance feedback for generative retrieval. Under an alternate training framework, the generative retrieval model gradually acquires higher-quality ranking signals to optimize the model. Experimental results show that our approach increasing Recall@1 by 12.9% with respect to the baselines on MS MARCO dataset.<\/jats:p>","DOI":"10.1145\/3603167","type":"journal-article","created":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T12:47:41Z","timestamp":1717418861000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["ROGER: Ranking-Oriented Generative Retrieval"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3530-3787","authenticated-orcid":false,"given":"Yujia","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Information, Renmin University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0527-6095","authenticated-orcid":false,"given":"Jing","family":"Yao","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9781-948X","authenticated-orcid":false,"given":"Zhicheng","family":"Dou","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7392-0524","authenticated-orcid":false,"given":"Yiteng","family":"Tu","sequence":"additional","affiliation":[{"name":"School of Information, Renmin University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3576-8920","authenticated-orcid":false,"given":"Ledell","family":"Wu","sequence":"additional","affiliation":[{"name":"Beijing Academy of Artificial Intelligence, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6097-7807","authenticated-orcid":false,"given":"Tat-Seng","family":"Chua","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9777-9676","authenticated-orcid":false,"given":"Ji-Rong","family":"Wen","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,10,22]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Yang Bai Xiaoguang Li Gang Wang Chaoliang Zhang Lifeng Shang Jun Xu Zhaowei Wang Fangshan Wang and Qun Liu. 2020. SparTerm: Learning Term-Based Sparse Representation for Fast Text Retrieval. arXiv:2010.00768. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.2010.00768"},{"key":"e_1_3_1_3_2","first-page":"31668","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","volume":"35","author":"Bevilacqua Michele","year":"2022","unstructured":"Michele Bevilacqua, Giuseppe Ottaviano, Patrick Lewis, Wen-tau Yih, Sebastian Riedel, and Fabio Petroni. 2022. Autoregressive Search Engines: Generating Substrings as Document Identifiers. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 35, 31668\u201331683."},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10791-011-9172-x"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102363"},{"issue":"23","key":"e_1_3_1_6_2","first-page":"81","article-title":"From Ranknet to Lambdarank to Lambdamart: An Overview","volume":"11","author":"Burges Christopher J. C.","year":"2010","unstructured":"Christopher J. C. Burges. 2010. From Ranknet to Lambdarank to Lambdamart: An Overview. Learning 11, 23\u2013581 (2010), 81.","journal-title":"Learning"},{"key":"e_1_3_1_7_2","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR)","author":"Cao Nicola De","year":"2021","unstructured":"Nicola De Cao, Gautier Izacard, Sebastian Riedel, and Fabio Petroni. 2021. Autoregressive Entity Retrieval. In Proceedings of the International Conference on Learning Representations (ICLR). Retrieved from OpenReview.net."},{"key":"e_1_3_1_8_2","first-page":"129","volume-title":"Proceedings of the 24th International Conference on Machine Learning (ICML) (ACM International Conference Proceeding Series","volume":"227","author":"Cao Zhe","year":"2007","unstructured":"Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. 2007. Learning to Rank: From Pairwise Approach to Listwise Approach. In Proceedings of the 24th International Conference on Machine Learning (ICML) (ACM International Conference Proceeding Series, Vol. 227). ACM, 129\u2013136."},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531827"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557271"},{"key":"e_1_3_1_11_2","first-page":"5199","volume-title":"Proceedings of the Advances in Neural Information Processing Systems (NeurIPS)","author":"Chen Qi","year":"2021","unstructured":"Qi Chen, Bing Zhao, Haidong Wang, Mingqin Li, Chuanjie Liu, Zengzhong Li, Mao Yang, and Jingdong Wang. 2021. SPANN: Highly-efficient Billion-Scale Approximate Nearest Neighborhood Search. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS). 5199\u20135212."},{"key":"e_1_3_1_12_2","first-page":"20","volume-title":"Proceedings of the Text Retrieval Conference (TREC)","volume":"500","author":"Clarke Charles L. A.","year":"2009","unstructured":"Charles L. A. Clarke, Nick Craswell, and Ian Soboroff. 2009. Overview of the TREC 2009 Web Track. In Proceedings of the Text Retrieval Conference (TREC) (NIST Special Publication, Vol. 500-278). National Institute of Standards and Technology (NIST). 20\u201329."},{"key":"e_1_3_1_13_2","unstructured":"Zhuyun Dai and Jamie Callan. 2019. Context-Aware Sentence\/Passage Term Importance Estimation for First Stage Retrieval. arXiv:1910.10687. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.1910.10687"},{"key":"e_1_3_1_14_2","first-page":"1897","volume-title":"Proceedings of the Web Conference (WWW \u201920)","author":"Dai Zhuyun","year":"2020","unstructured":"Zhuyun Dai and Jamie Callan. 2020. Context-Aware Document Term Weighting for Ad-Hoc Search. In Proceedings of the Web Conference (WWW \u201920). ACM\/IW3C2, 1897\u20131907."},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080832"},{"key":"e_1_3_1_16_2","first-page":"4171","volume-title":"Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT \u201919)","volume":"1","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT \u201919), Vol. 1 (Long and Short Papers). Association for Computational Linguistics, 4171\u20134186."},{"key":"e_1_3_1_17_2","doi-asserted-by":"crossref","unstructured":"Thibault Formal Carlos Lassance Benjamin Piwowarski and St\u00e9phane Clinchant. 2021a. SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval. arXiv:2109.10086. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.2109.10086","DOI":"10.1145\/3404835.3463098"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463098"},{"key":"e_1_3_1_19_2","first-page":"146","volume-title":"Proceedings of the Advances in Information Retrieval - 43rd European Conference on IR Research (ECIR \u201921), Part I (Lecture Notes in Computer Science","volume":"12656","author":"Gao Luyu","year":"2021","unstructured":"Luyu Gao, Zhuyun Dai, Tongfei Chen, Zhen Fan, Benjamin Van Durme, and Jamie Callan. 2021. Complement Lexical Retrieval Model with Semantic Residual Embeddings. In Proceedings of the Advances in Information Retrieval - 43rd European Conference on IR Research (ECIR \u201921), Part I (Lecture Notes in Computer Science, Vol. 12656). Springer, 146\u2013160."},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.240"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/2983323.2983769"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462891"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.550"},{"key":"e_1_3_1_24_2","unstructured":"Saar Kuzi Mingyang Zhang Cheng Li Michael Bendersky and Marc Najork. 2020. Leveraging Semantic and Lexical Matching to Improve the Recall of Document Retrieval Systems: A Hybrid Approach. arXiv:2010.01195. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.2010.01195"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00276"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p19-1612"},{"key":"e_1_3_1_27_2","doi-asserted-by":"crossref","first-page":"7871","DOI":"10.18653\/v1\/2020.acl-main.703","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL)","author":"Lewis Mike","year":"2020","unstructured":"Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL). Association for Computational Linguistics, 7871\u20137880."},{"key":"e_1_3_1_28_2","unstructured":"Xiaoxi Li Zhicheng Dou Yujia Zhou and Fangchao Liu. 2024a. Towards a Unified Language Model for Knowledge-Intensive Tasks Utilizing External Corpus. arXiv:2402.01176. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.2402.01176"},{"key":"e_1_3_1_29_2","unstructured":"Xiaoxi Li Jiajie Jin Yujia Zhou Yuyao Zhang Peitian Zhang Yutao Zhu and Zhicheng Dou. 2024b. From Matching to Generation: A Survey on Generative Information Retrieval. arXiv:2404.14851. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.2404.14851"},{"key":"e_1_3_1_30_2","first-page":"8688","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"38","author":"Li Xiaoxi","year":"2024","unstructured":"Xiaoxi Li, Yujia Zhou, and Zhicheng Dou. 2024c. UniGen: A Unified Generative Framework for Retrieval and Question Answering with Large Language Models. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 8688\u20138696."},{"key":"e_1_3_1_31_2","first-page":"2890","volume-title":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL) (Vol. 1: Long Papers)","author":"Liu Yixin","year":"2022","unstructured":"Yixin Liu, Pengfei Liu, Dragomir R. Radev, and Graham Neubig. 2022. BRIO: Bringing Order to Abstractive Summarization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL) (Vol. 1: Long Papers). Association for Computational Linguistics, 2890\u20132903."},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2889473"},{"key":"e_1_3_1_33_2","unstructured":"Sanket Vaibhav Mehta Jai Prakash Gupta Yi Tay Mostafa Dehghani Vinh Q. Tran Jinfeng Rao Marc Najork Emma Strubell and Donald Metzler. 2022. DSI++: Updating Transformer Memory with New Documents. arXiv:2212.09744. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.2212.09744"},{"key":"e_1_3_1_34_2","first-page":"13:1","volume-title":"Proceedings of the SIGIR Forum","volume":"55","author":"Metzler Donald","year":"2021","unstructured":"Donald Metzler, Yi Tay, Dara Bahri, and Marc Najork. 2021. Rethinking Search: Making Domain Experts Out of Dilettantes. Proceedings of the SIGIR Forum 55, 1 (2021), 13:1\u201313:27."},{"key":"e_1_3_1_35_2","first-page":"1","volume-title":"Proceedings of the 1st International Conference on Learning Representations (ICLR \u201913)","author":"Mikolov Tom\u00e1s","year":"2013","unstructured":"Tom\u00e1s Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. In Proceedings of the 1st International Conference on Learning Representations (ICLR \u201913), Workshop Track Proceedings. 1\u201312."},{"key":"e_1_3_1_36_2","volume-title":"Proceedings of the Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches 2016 Co-Located with the 30th Annual Conference on Neural Information Processing Systems (NIPS \u201916)","volume":"1773","author":"Nguyen Tri","year":"2016","unstructured":"Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016. MS MARCO: A Human Generated MAchine Reading COmprehension Dataset. In Proceedings of the Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches 2016 Co-Located with the 30th Annual Conference on Neural Information Processing Systems (NIPS \u201916) (CEUR Workshop Proceedings, Vol. 1773): Retrieved from CEUR-WS.org."},{"key":"e_1_3_1_37_2","unstructured":"Jianmo Ni Gustavo Hern\u00e1ndez \u00c1brego Noah Constant Ji Ma Keith B. Hall Daniel Cer and Yinfei Yang. 2021. Sentence-T5: Scalable Sentence Encoders from Pre-Trained Text-to-Text Models. arXiv:2108.08877. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.2108.08877"},{"key":"e_1_3_1_38_2","first-page":"708","volume-title":"Proceedings of the Empirical Methods in Natural Language Processing (EMNLP) (Findings)","author":"Nogueira Rodrigo","year":"2020","unstructured":"Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin. 2020. Document Ranking with a Pretrained Sequence-to-Sequence Model. In Proceedings of the Empirical Methods in Natural Language Processing (EMNLP) (Findings). Association for Computational Linguistics, 708\u2013718."},{"key":"e_1_3_1_39_2","unstructured":"Rodrigo Nogueira Jimmy Lin and AI Epistemic. 2019a. From doc2query to docTTTTTquery. Online Preprint 6 (2019). Retrieved from https:\/\/cs.uwaterloo.ca\/\u223cjimmylin\/publications\/Nogueira_Lin_2019_docTTTTTquery-v2.pdf"},{"key":"e_1_3_1_40_2","unstructured":"Rodrigo Nogueira Wei Yang Jimmy Lin and Kyunghyun Cho. 2019b. Document Expansion by Query Prediction. arXiv:1904.08375. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.1904.08375"},{"key":"e_1_3_1_41_2","first-page":"1532","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP \u201914), A Meeting of SIGDAT, a Special Interest Group of the ACL","author":"Pennington Jeffrey","year":"2014","unstructured":"Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global Vectors for Word Representation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP \u201914), A Meeting of SIGDAT, a Special Interest Group of the ACL. ACL, 1532\u20131543."},{"key":"e_1_3_1_42_2","first-page":"140:1","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 (2020), 140:1\u2013140:67.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1561\/1500000019"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/2505515.2505671"},{"key":"e_1_3_1_45_2","doi-asserted-by":"crossref","unstructured":"Weizhou Shen Yeyun Gong Yelong Shen Song Wang Xiaojun Quan Nan Duan and Weizhu Chen. 2022. Joint Generator-Ranker Learning for Natural Language Generation. arXiv:2206.13974. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.2206.13974","DOI":"10.18653\/v1\/2023.findings-acl.486"},{"key":"e_1_3_1_46_2","first-page":"1","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","volume":"36","author":"Sun Weiwei","year":"2023","unstructured":"Weiwei Sun, Lingyong Yan, Zheng Chen, Shuaiqiang Wang, Haichao Zhu, Pengjie Ren, Zhumin Chen, Dawei Yin, Maarten de Rijke, and Zhaochun Ren. 2023. Learning to Tokenize for Generative Retrieval. Proceedings of the Advances in Neural Information Processing Systems, Vol. 36. 1\u201317."},{"key":"e_1_3_1_47_2","first-page":"21831","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","volume":"35","author":"Tay Yi","year":"2022","unstructured":"Yi Tay, Vinh Q. Tran, Mostafa Dehghani, Jianmo Ni, Dara Bahri, Harsh Mehta, Zhen Qin, Kai Hui, Zhe Zhao, Jai Prakash Gupta, Tal Schuster, William W. Cohen, and Donald Metzler. 2022. Transformer Memory as a Differentiable Search Index. Proceedings of the Advances in Neural Information Processing Systems, Vol. 35, 21831\u201321843."},{"key":"e_1_3_1_48_2","first-page":"25600","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","volume":"35","author":"Wang Yujing","year":"2022","unstructured":"Yujing Wang, Yingyan Hou, Haonan Wang, Ziming Miao, Shibin Wu, Hao Sun, Qi Chen, Yuqing Xia, Chengmin Chi, Guoshuai Zhao, Zheng Liu, Xing Xie, Hao Allen Sun, Weiwei Deng, Qi Zhang, and Mao Yang. 2022. A Neural Corpus Indexer for Document Retrieval. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 35, 25600\u201325614."},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614993"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531799"},{"key":"e_1_3_1_51_2","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR)","author":"Xiong Lee","year":"2021","unstructured":"Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul N. Bennett, Junaid Ahmed, and Arnold Overwijk. 2021. Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval. In Proceedings of the International Conference on Learning Representations (ICLR). Retrieved from OpenReview.net."},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482358"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462880"},{"key":"e_1_3_1_54_2","unstructured":"Jingtao Zhan Jiaxin Mao Yiqun Liu Min Zhang and Shaoping Ma. 2020. RepBERT: Contextualized Text Embeddings for First-Stage Retrieval. arXiv:2006.15498. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.2006.15498"},{"key":"e_1_3_1_55_2","unstructured":"Peitian Zhang Zheng Liu Yujia Zhou Zhicheng Dou and Zhao Cao. 2023. Term-Sets Can Be Strong Document Identifiers for Auto-Regressive Search Engines. arXiv:2305.13859. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.2305.13859"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767700"},{"key":"e_1_3_1_57_2","first-page":"12481","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)","author":"Zhou Yujia","year":"2023","unstructured":"Yujia Zhou, Zhicheng Dou, and Ji-Rong Wen. 2023a. Enhancing Generative Retrieval with Reinforcement Learning from Relevance Feedback. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, 12481\u201312490."},{"key":"e_1_3_1_58_2","unstructured":"Yujia Zhou Jing Yao Zhicheng Dou Ledell Wu and Ji-Rong Wen. 2022a. DynamicRetriever: A Pre-training Model-based IR System with Neither Sparse nor Dense Index. arXiv:2203.00537. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.2203.00537"},{"key":"e_1_3_1_59_2","unstructured":"Yujia Zhou Jing Yao Zhicheng Dou Ledell Wu Peitian Zhang and Ji-Rong Wen. 2022b. Ultron: An Ultimate Retriever on Corpus with a Model-Based Indexer. arXiv:2208.09257. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.2208.09257"},{"key":"e_1_3_1_60_2","first-page":"1","article-title":"WebUltron: An Ultimate Retriever on Webpages Under the Model-Centric Paradigm","author":"Zhou Yujia","year":"2023","unstructured":"Yujia Zhou, Jing Yao, Ledell Wu, Zhicheng Dou, and Ji-Rong Wen. 2023b. WebUltron: An Ultimate Retriever on Webpages Under the Model-Centric Paradigm. IEEE Transactions on Knowledge and Data Engineering (2023). Early Access, 1\u201312.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_61_2","unstructured":"Shengyao Zhuang Houxing Ren Linjun Shou Jian Pei Ming Gong Guido Zuccon and Daxin Jiang. 2022. Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation. arXiv:2003.06713. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.2003.06713"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3603167","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3603167","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:49:09Z","timestamp":1750286949000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3603167"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,22]]},"references-count":60,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,11,30]]}},"alternative-id":["10.1145\/3603167"],"URL":"https:\/\/doi.org\/10.1145\/3603167","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,22]]},"assertion":[{"value":"2023-05-15","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-05-13","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}