{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:02:01Z","timestamp":1750309321129,"version":"3.41.0"},"reference-count":30,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGIR Forum"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:p>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 together around the topic of generative AI that gathered attention in 2022 and 2023 with large language models and diffusion models. Given the novelty of the topic, the workshop was focused around multi-sided discussions, namely panels and poster sessions of the accepted proceedings papers. Two main research outcomes are the proceedings of the workshop1 and the potential research directions discussed in this report.<\/jats:p>\n          <jats:p>\n            <jats:bold>Date<\/jats:bold>\n            : 27 July 2023.\n          <\/jats:p>\n          <jats:p>\n            <jats:bold>Website<\/jats:bold>\n            : https:\/\/coda.io\/@sigir\/gen-ir.\n          <\/jats:p>","DOI":"10.1145\/3642979.3642995","type":"journal-article","created":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T17:05:12Z","timestamp":1705943112000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Report on the 1st Workshop on Generative Information Retrieval (Gen-IR 2023) at SIGIR 2023"],"prefix":"10.1145","volume":"57","author":[{"given":"Gabriel","family":"B\u00e9n\u00e9dict","sequence":"first","affiliation":[{"name":"IRLab &amp; RTL NL, University of Amsterdam, The Netherlands"}]},{"given":"Ruqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"ICT, Chinese Academy of Sciences, China"}]},{"given":"Donald","family":"Metzler","sequence":"additional","affiliation":[{"name":"Google Research, USA"}]},{"given":"Andrew","family":"Yates","sequence":"additional","affiliation":[{"name":"IRLab, University of Amsterdam, The Netherlands"}]},{"given":"Romain","family":"Deffayet","sequence":"additional","affiliation":[{"name":"IRLab &amp; Naver Labs Europe, University of Amsterdam &amp; Naver, The Netherlands &amp; France"}]},{"given":"Philipp","family":"Hager","sequence":"additional","affiliation":[{"name":"Mercury Machine Learning Lab, University of Amsterdam, The Netherlands"}]},{"given":"Sami","family":"Jullien","sequence":"additional","affiliation":[{"name":"AI for Retail Lab, University of Amsterdam, The Netherlands"}]}],"member":"320","published-online":{"date-parts":[[2024,1,22]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Retrievability bias estimation using synthetically generated queries. Gen-IR@SIGIR 2023: The First Workshop on Generative Information Retrieval","author":"Abolghasemi Amin","year":"2023","unstructured":"Amin Abolghasemi, Suzan Verberne, Arian Askari, and Leif Azzopardi. Retrievability bias estimation using synthetically generated queries. Gen-IR@SIGIR 2023: The First Workshop on Generative Information Retrieval, 2023."},{"key":"e_1_2_1_2_1","volume-title":"Generating synthetic documents for cross-encoder re-rankers: A comparative study of chatgpt and human experts. Gen-IR@SIGIR 2023: The First Workshop on Generative Information Retrieval","author":"Askari Arian","year":"2023","unstructured":"Arian Askari, Mohammad Aliannejadi, Evangelos Kanoulas, and Suzan Verberne. Generating synthetic documents for cross-encoder re-rankers: A comparative study of chatgpt and human experts. Gen-IR@SIGIR 2023: The First Workshop on Generative Information Retrieval, 2023."},{"key":"e_1_2_1_3_1","volume-title":"Mikita Balesni, Max Kaufmann, Meg Tong, Tomasz Korbak, Daniel Kokotajlo, and Owain Evans. Taken out of context: On measuring situational awareness in llms","author":"Berglund Lukas","year":"2023","unstructured":"Lukas Berglund, Asa Cooper Stickland, Mikita Balesni, Max Kaufmann, Meg Tong, Tomasz Korbak, Daniel Kokotajlo, and Owain Evans. Taken out of context: On measuring situational awareness in llms, 2023."},{"key":"e_1_2_1_4_1","first-page":"31668","volume-title":"Advances in Neural Information Processing Systems","volume":"35","author":"Bevilacqua Michele","year":"2022","unstructured":"Michele Bevilacqua, Giuseppe Ottaviano, Patrick Lewis, Scott Yih, Sebastian Riedel, and Fabio Petroni. Autoregressive search engines: Generating substrings as document identifiers. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems, volume 35, pages 31668--31683. Curran Associates, Inc., 2022. URL https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2022\/file\/cd88d62a2063fdaf7ce6f9068fb15dcd-Paper-Conference.pdf."},{"key":"e_1_2_1_5_1","volume-title":"Autoregressive entity retrieval. CoRR, abs\/2010.00904","author":"Cao Nicola De","year":"2020","unstructured":"Nicola De Cao, Gautier Izacard, Sebastian Riedel, and Fabio Petroni. Autoregressive entity retrieval. CoRR, abs\/2010.00904, 2020. URL https:\/\/arxiv.org\/abs\/2010.00904."},{"key":"e_1_2_1_6_1","volume-title":"Gpt-4 synthetic data improves generalizability for contract clause retrieval. Gen-IR@SIGIR 2023: The First Workshop on Generative Information Retrieval","author":"Gao Shang","year":"2023","unstructured":"Shang Gao, Divyanshu Murli, Javed Qadrud-Din, and Martin Gajek. Gpt-4 synthetic data improves generalizability for contract clause retrieval. Gen-IR@SIGIR 2023: The First Workshop on Generative Information Retrieval, 2023."},{"key":"e_1_2_1_7_1","volume-title":"Double q-learning. Advances in neural information processing systems, 23","author":"Hasselt Hado","year":"2010","unstructured":"Hado Hasselt. Double q-learning. Advances in neural information processing systems, 23, 2010."},{"key":"e_1_2_1_8_1","series-title":"Proceedings of Machine Learning Research","first-page":"2790","volume-title":"Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. Parameter-efficient transfer learning for NLP","author":"Houlsby Neil","year":"2019","unstructured":"Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. Parameter-efficient transfer learning for NLP. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 2790--2799. PMLR, 09--15 Jun 2019."},{"key":"e_1_2_1_9_1","volume-title":"Query expansion by prompting large language models. Gen-IR@SIGIR 2023: The First Workshop on Generative Information Retrieval","author":"Jagerman Rolf","year":"2023","unstructured":"Rolf Jagerman, Honglei Zhuang, Zhen Qin, Xuanhui Wang, and Michael Bendersky. Query expansion by prompting large language models. 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