Gen-IR @ SIGIR 2024: The Second Workshop on Generative Information Retrieval
July 2024
Pages 3029 - 3032
Abstract
Generative information retrieval (Gen-IR) is a fast-growing interdisciplinary research area that investigates how to leverage advances in generative Artificial Intelligence (AI) to improve information retrieval systems. Gen-IR has attracted interest from the information retrieval, natural language processing, and machine learning communities, among others. Since the dawn of Gen-IR last year, there has been an explosion of Gen-IR systems that have launched and are now widely used. Interest in this area across academia and industry is only expected to continue to grow as new research challenges and application opportunities arise. The goal of this proposed workshop, The Second Workshop on Generative Information Retrieval (Gen-IR @ SIGIR 2024) is to provide an interactive venue for exploring a broad range of foundational and applied Gen-IR research. The workshop will focus on tasks such as generative document retrieval, grounded answer generation, generative recommendation, and generative knowledge graphs, all through the lens of model training, model behavior, and broader issues. The workshop will be highly interactive, favoring panel discussions, poster sessions, and roundtable discussions over one-sided keynotes and paper talks.
References
[1]
Garbiel Bénédict, Ruqing Zhang, and Donald Metzler. 2023a. Gen-IR@SIGIR 2023: The First Workshop on Generative Information Retrieval. In SIGIR (SIGIR '23). Association for Computing Machinery, New York, NY, USA, 3460--3463. https://doi.org/10.1145/3539618.3591923
[2]
Garbiel Bénédict, Ruqing Zhang, Donald Metzler, Andrew Yates, Romain Deffayet, Philipp Hager, and Sami Jullien. 2023b. Report on the 1st Workshop on Generative Information Retrieval (Gen-IR 2023) at SIGIR 2023. In SIGIR Forum Decembver 2023, Volume 57 Number 2 (SIGIR '23). Association for Computing Machinery, New York, NY, USA. https://sigir.org/wp-content/uploads/2023/12/p13.pdf
[3]
Bernd Bohnet, Vinh Q. Tran, Pat Verga, Roee Aharoni, Daniel Andor, Livio Baldini Soares, Jacob Eisenstein, Kuzman Ganchev, Jonathan Herzig, Kai Hui, Tom Kwiatkowski, Ji Ma, Jianmo Ni, Tal Schuster, William W. Cohen, Michael Collins, Dipanjan Das, Donald Metzler, Slav Petrov, and Kellie Webster. 2022. Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models. https://doi.org/10.48550/ARXIV.2212.08037
[4]
Gabriel Bénédict, Olivier Jeunen, Samuele Papa, Samarth Bhargav, Daan Odijk, and Maarten de Rijke. 2023. RecFusion: A Binomial Diffusion Process for 1D Data for Recommendation. arxiv: 2306.08947 [cs.IR]
[5]
Nicola De Cao, Gautier Izacard, Sebastian Riedel, and Fabio Petroni. 2020. Autoregressive Entity Retrieval. CoRR, Vol. abs/2010.00904 (2020). https://arxiv.org/abs/2010.00904
[6]
Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Yiqun Liu, Yixing Fan, and Xueqi Cheng. 2022. CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks. In CIKM. 191--200.
[7]
Roi Cohen, Mor Geva, Jonathan Berant, and Amir Globerson. 2023. Crawling The Internal Knowledge-Base of Language Models. In Findings of EACL, Andreas Vlachos and Isabelle Augenstein (Eds.). Association for Computational Linguistics, Dubrovnik, Croatia, 1856--1869. https://doi.org/10.18653/v1/2023.findings-eacl.139
[8]
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Qianyu Guo, Meng Wang, and Haofen Wang. 2024. Retrieval-Augmented Generation for Large Language Models: A Survey. arxiv: 2312.10997 [cs.CL]
[9]
Zihao Li, Aixin Sun, and Chenliang Li. 2023. DiffuRec: A Diffusion Model for Sequential Recommendation. ACM Trans. Inf. Syst., Vol. 42, 3, Article 66 (dec 2023), 28 pages. https://doi.org/10.1145/3631116
[10]
Haokai Ma, Ruobing Xie, Lei Meng, Xin Chen, Xu Zhang, Leyu Lin, and Zhanhui Kang. 2024. Plug-in Diffusion Model for Sequential Recommendation. arxiv: 2401.02913 [cs.IR]
[11]
Sanket Mehta, Jai Gupta, Yi Tay, Mostafa Dehghani, Vinh Tran, Jinfeng Rao, Marc Najork, Emma Strubell, and Donald Metzler. 2023. DSI: Updating Transformer Memory with New Documents. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 8198--8213.
[12]
Donald Metzler, Yi Tay, Dara Bahri, and Marc Najork. 2021. Rethinking Search: Making Domain Experts out of Dilettantes. SIGIR Forum, Vol. 55, 1, Article 13 (jul 2021), 27 pages. https://doi.org/10.1145/3476415.3476428
[13]
Ronak Pradeep, Kai Hui, Jai Gupta, Adam Lelkes, Honglei Zhuang, Jimmy Lin, Donald Metzler, and Vinh Tran. 2023. How Does Generative Retrieval Scale to Millions of Passages?. In EMNLP, Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Association for Computational Linguistics, Singapore, 1305--1321. https://doi.org/10.18653/v1/2023.emnlp-main.83
[14]
Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, et al. 2023. Large language models are effective text rankers with pairwise ranking prompting. arXiv preprint arXiv:2306.17563 (2023).
[15]
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J Liu, et al. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., Vol. 21, 140 (2020), 1--67.
[16]
Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan Hulikal Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Q. Tran, Jonah Samost, Maciej Kula, Ed H. Chi, and Maheswaran Sathiamoorthy. 2023. Recommender Systems with Generative Retrieval. In Thirty-seventh Conference on Neural Information Processing Systems. https://openreview.net/forum?id=BJ0fQUU32w
[17]
Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Eric Michael Smith, Y-Lan Boureau, and Jason Weston. 2021. Recipes for Building an Open-Domain Chatbot. In EACL. Association for Computational Linguistics, Online, 300--325. https://doi.org/10.18653/v1/2021.eacl-main.24
[18]
Yi Tay, Vinh Q. Tran, Mostafa Dehghani, Jianmo Ni, Dara Bahri, Harsh Mehta, Zhen Qin, Kai Hui, Zhe Zhao, Jai Gupta, Tal Schuster, William W. Cohen, and Donald Metzler. 2022. Transformer Memory as a Differentiable Search Index. In Advances in Neural Information Processing Systems, Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho (Eds.). https://openreview.net/forum?id=Vu-B0clPfq
[19]
Joojo Walker, Ting Zhong, Fengli Zhang, Qiang Gao, and Fan Zhou. 2022. Recommendation via Collaborative Diffusion Generative Model. In Knowledge Science, Engineering and Management, Gerard Memmi, Baijian Yang, Linghe Kong, Tianwei Zhang, and Meikang Qiu (Eds.). Springer International Publishing, Cham.
[20]
Dongsheng Wang, Zhiqiang Ma, Armineh Nourbakhsh, Kang Gu, and Sameena Shah. 2023b. Docgraphlm: Documental graph language model for information extraction. In SIGIR. 1944--1948.
[21]
Dongsheng Wang, Zhiqiang Ma, Armineh Nourbakhsh, Kang Gu, and Sameena Shah. 2024. DocGraphLM: Documental Graph Language Model for Information Extraction. arxiv: 2401.02823 [cs.CL]
[22]
Peng Wang, Ningyu Zhang, Xin Xie, Yunzhi Yao, Bozhong Tian, Mengru Wang, Zekun Xi, Siyuan Cheng, Kangwei Liu, Guozhou Zheng, and Huajun Chen. 2023d. EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models. arxiv: 2308.07269 [cs.CL]
[23]
Wenjie Wang, Xinyu Lin, Fuli Feng, Xiangnan He, and Tat-Seng Chua. 2023a. Generative Recommendation: Towards Next-generation Recommender Paradigm. arxiv: 2304.03516 [cs.IR]
[24]
Wenjie Wang, Yiyan Xu, Fuli Feng, Xinyu Lin, Xiangnan He, and Tat-Seng Chua. 2023c. Diffusion Recommender Model. In SIGIR (SIGIR '23). Association for Computing Machinery, New York, NY, USA, 832--841. https://doi.org/10.1145/3539618.3591663
[25]
Liu Yang, Junjie Hu, Minghui Qiu, Chen Qu, Jianfeng Gao, W Bruce Croft, Xiaodong Liu, Yelong Shen, and Jingjing Liu. 2019. A hybrid retrieval-generation neural conversation model. In Proceedings of the 28th ACM international conference on information and knowledge management. 1341--1350.
[26]
Junjie Zhang, Ruobing Xie, Yupeng Hou, Wayne Xin Zhao, Leyu Lin, and Ji-Rong Wen. 2023. Recommendation as instruction following: A large language model empowered recommendation approach. arXiv preprint arXiv:2305.07001 (2023).
[27]
Yunqin Zhu, Chao Wang, and Hui Xiong. 2023. Towards Graph-Aware Diffusion Modeling for Collaborative Filtering. arxiv: 2311.08744 [cs.IR]
Index Terms
- Gen-IR @ SIGIR 2024: The Second Workshop on Generative Information Retrieval
Recommendations
Report on the Second SIGIR Workshop on Neural Information Retrieval (Neu-IR'17)
The second SIGIR workshop on neural information retrieval (Neu-IR?17) took place on August 11, 2017, in Tokyo, Japan. Following the successful 2016 edition, the workshop continued to serve as a forum for academic and industrial researchers to present ...
Report on the 1st Workshop on Generative Information Retrieval (Gen-IR 2023) at SIGIR 2023
The first edition of the workshop on Generative Information Retrieval (Gen-IR 2023) took place in July 2023 in a hybrid fashion, co-located with the ACM SIGIR Conference 2023 in Taipei (SIGIR 2023). The aim was to bring information retrieval researchers ...
Report on the SIGIR 2016 Workshop on Neural Information Retrieval (Neu-IR)
The SIGIR 2016 workshop on Neural Information Retrieval (Neu-IR) took place on 21 July, 2016 in Pisa. The goal of the Neu-IR (pronounced "New IR") workshop was to serve as a forum for academic and industrial researchers, working at the intersection of ...
Comments
Information & Contributors
Information
Published In
July 2024
3164 pages
ISBN:9798400704314
DOI:10.1145/3626772
- General Chairs:
- Grace Hui Yang,
- Hongning Wang,
- Sam Han,
- Program Chairs:
- Claudia Hauff,
- Guido Zuccon,
- Yi Zhang
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution-NoDerivatives International 4.0 License.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 July 2024
Check for updates
Author Tags
Qualifiers
- Short-paper
Conference
SIGIR 2024
Sponsor:
SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 14 - 18, 2024
Washington DC, USA
Acceptance Rates
Overall Acceptance Rate 792 of 3,983 submissions, 20%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 3Total Downloads
- Downloads (Last 12 months)3
- Downloads (Last 6 weeks)3
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in