{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:18:44Z","timestamp":1784179124884,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":88,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T00:00:00Z","timestamp":1720569600000},"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":[],"published-print":{"date-parts":[[2024,7,10]]},"DOI":"10.1145\/3626772.3657842","type":"proceedings-article","created":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T12:40:05Z","timestamp":1720701605000},"page":"14-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["\"In-Context Learning\" or: How I learned to stop worrying and love \"Applied Information Retrieval\""],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5446-8328","authenticated-orcid":false,"given":"Andrew","family":"Parry","sequence":"first","affiliation":[{"name":"University of Glasgow, Glasgow, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0050-7138","authenticated-orcid":false,"given":"Debasis","family":"Ganguly","sequence":"additional","affiliation":[{"name":"University of Glasgow, Glasgow, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6156-5337","authenticated-orcid":false,"given":"Manish","family":"Chandra","sequence":"additional","affiliation":[{"name":"University of Glasgow, Glasgow, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,7,11]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591960"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1571941.1572031"},{"key":"e_1_3_2_1_3_1","unstructured":"Simran Arora Avanika Narayan Mayee F. Chen Laurel Orr Neel Guha Kush Bhatia Ines Chami Frederic Sala and Christopher R\u00e9. 2022. Ask Me Anything: A simple strategy for prompting language models. arxiv: 2210.02441 [cs.CL]"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401188"},{"key":"e_1_3_2_1_5_1","unstructured":"Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems Vol. 33 (2020) 1877--1901."},{"key":"e_1_3_2_1_6_1","volume-title":"Proc. of TREC","author":"Carterette Ben","year":"2014","unstructured":"Ben Carterette, Evangelos Kanoulas, Mark M. Hall, and Paul D. Clough. [n.,d.]. Overview of the TREC 2014 Session Track. In Proc. of TREC 2014."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Anirban Chakraborty Debasis Ganguly and Owen Conlan. 2020. Retrievability based Document Selection for Relevance Feedback with Automatically Generated Query Variants. In CIKM. ACM 125--134.","DOI":"10.1145\/3340531.3412032"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390334.1390446"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.6028\/NIST.SP.500-278.web-overview"},{"key":"e_1_3_2_1_10_1","volume-title":"Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '02)","author":"Cronen-Townsend Steve","unstructured":"Steve Cronen-Townsend, Yun Zhou, and W. Bruce Croft. 2002. Predicting Query Performance. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '02). Association for Computing Machinery, New York, NY, USA, 299--306."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Florin Cuconasu Giovanni Trappolini Federico Siciliano Simone Filice Cesare Campagnano Yoelle Maarek Nicola Tonellotto and Fabrizio Silvestri. 2024. The Power of Noise: Redefining Retrieval for RAG Systems. arxiv: 2401.14887 [cs.IR]","DOI":"10.1145\/3626772.3657834"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2559170"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331303"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498491"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3545112"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/1060745.1060764"},{"key":"e_1_3_2_1_17_1","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","volume":"1","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019a. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 4171--4186."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19--1423"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/1277741.1277841"},{"key":"e_1_3_2_1_20_1","unstructured":"Qingxiu Dong Lei Li Damai Dai Ce Zheng Zhiyong Wu Baobao Chang Xu Sun Jingjing Xu Lei Li and Zhifang Sui. 2023. A Survey on In-context Learning. arxiv: 2301.00234 [cs.CL]"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591625"},{"key":"e_1_3_2_1_22_1","volume-title":"Jones","author":"Ganguly Debasis","year":"2013","unstructured":"Debasis Ganguly, Manisha Ganguly, Johannes Leveling, and Gareth J. F. Jones. 2013a. TopicVis: a GUI for topic-based feedback and navigation. In SIGIR. ACM, 1103--1104."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10791-018-9329-y"},{"key":"e_1_3_2_1_24_1","volume-title":"Jones","author":"Ganguly Debasis","year":"2013","unstructured":"Debasis Ganguly, Johannes Leveling, and Gareth J. F. Jones. 2013b. An LDA-smoothed relevance model for document expansion: a case study for spoken document retrieval. In SIGIR. ACM, 1057--1060."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Debasis Ganguly and Emine Yilmaz. 2023. Query-specific Variable Depth Pooling via Query Performance Prediction. In SIGIR. ACM 2303--2307.","DOI":"10.1145\/3539618.3592046"},{"key":"e_1_3_2_1_26_1","volume-title":"The Pile: An 800GB Dataset of Diverse Text for Language Modeling. arXiv preprint arXiv:2101.00027","author":"Gao Leo","year":"2020","unstructured":"Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy. 2020. The Pile: An 800GB Dataset of Diverse Text for Language Modeling. arXiv preprint arXiv:2101.00027 (2020)."},{"key":"e_1_3_2_1_27_1","volume-title":"Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline. CoRR","author":"Gao Luyu","year":"2021","unstructured":"Luyu Gao, Zhuyun Dai, and Jamie Callan. 2021a. Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline. CoRR , Vol. abs\/2101.08751 (2021). showeprint[arXiv]2101.08751 https:\/\/arxiv.org\/abs\/2101.08751"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/2023.ACL-LONG.99"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/2021.EMNLP-MAIN.552"},{"key":"e_1_3_2_1_30_1","volume-title":"Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research","volume":"304","author":"Gutmann Michael","year":"2010","unstructured":"Michael Gutmann and Aapo Hyv\u00e4rinen. 2010. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 9), Yee Whye Teh and Mike Titterington (Eds.). PMLR, Chia Laguna Resort, Sardinia, Italy, 297--304. https:\/\/proceedings.mlr.press\/v9\/gutmann10a.html"},{"key":"e_1_3_2_1_31_1","volume-title":"Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation. CoRR","author":"Sebastian","year":"2020","unstructured":"Sebastian Hofst\"a tter, Sophia Althammer, Michael Schr\u00f6 der, Mete Sertkan, and Allan Hanbury. 2020. Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation. CoRR , Vol. abs\/2010.02666 (2020). showeprint[arXiv]2010.02666 https:\/\/arxiv.org\/abs\/2010.02666"},{"key":"e_1_3_2_1_32_1","volume-title":"Knowledgeable prompt-tuning: Incorporating knowledge into prompt verbalizer for text classification. arXiv preprint arXiv:2108.02035","author":"Hu Shengding","year":"2021","unstructured":"Shengding Hu, Ning Ding, Huadong Wang, Zhiyuan Liu, Jingang Wang, Juanzi Li, Wei Wu, and Maosong Sun. 2021. Knowledgeable prompt-tuning: Incorporating knowledge into prompt verbalizer for text classification. arXiv preprint arXiv:2108.02035 (2021)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/2021.EACL-MAIN.74"},{"key":"e_1_3_2_1_34_1","article-title":"Atlas: Few-shot Learning with Retrieval Augmented Language Models","volume":"24","author":"Izacard Gautier","year":"2023","unstructured":"Gautier Izacard, Patrick S. H. Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 2023. Atlas: Few-shot Learning with Retrieval Augmented Language Models. J. Mach. Learn. Res. , Vol. 24 (2023), 251:1--251:43. http:\/\/jmlr.org\/papers\/v24\/23-0037.html","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_1_35_1","unstructured":"Albert Q. Jiang Alexandre Sablayrolles Arthur Mensch Chris Bamford Devendra Singh Chaplot Diego de las Casas Florian Bressand Gianna Lengyel Guillaume Lample Lucile Saulnier L\u00e9lio Renard Lavaud Marie-Anne Lachaux Pierre Stock Teven Le Scao Thibaut Lavril Thomas Wang Timoth\u00e9e Lacroix and William El Sayed. 2023. Mistral 7B. arxiv: 2310.06825 [cs.CL]"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/2020.EMNLP-MAIN.550"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401075"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"crossref","unstructured":"Itay Levy Ben Bogin and Jonathan Berant. 2023. Diverse Demonstrations Improve In-context Compositional Generalization. arxiv: 2212.06800 [cs.CL]","DOI":"10.18653\/v1\/2023.acl-long.78"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"e_1_3_2_1_40_1","volume-title":"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020","author":"Lewis Patrick S. H.","year":"2020","unstructured":"Patrick S. H. Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich K\u00fc ttler, Mike Lewis, Wen-tau Yih, Tim Rockt\"a schel, Sebastian Riedel, and Douwe Kiela. 2020b. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6--12, 2020, virtual, Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/6b493230205f780e1bc26945df7481e5-Abstract.html"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.trustnlp-1.1"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.385"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/2021.REPL4NLP-1.17"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","unstructured":"Jiachang Liu Dinghan Shen Yizhe Zhang Bill Dolan Lawrence Carin and Weizhu Chen. 2022. What Makes Good In-Context Examples for GPT-3?. In Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures Eneko Agirre Marianna Apidianaki and Ivan Vuli\u0107 (Eds.). Association for Computational Linguistics Dublin Ireland and Online 100--114. https:\/\/doi.org\/10.18653\/v1\/2022.deelio-1.10","DOI":"10.18653\/v1\/2022.deelio-1.10"},{"key":"e_1_3_2_1_45_1","unstructured":"Yinhan Liu Myle Ott Naman Goyal Jingfei Du Mandar Joshi Danqi Chen Omer Levy Mike Lewis Luke Zettlemoyer and Veselin Stoyanov. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach. http:\/\/arxiv.org\/abs\/1907.11692"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.556"},{"key":"e_1_3_2_1_47_1","unstructured":"Man Luo Xin Xu Yue Liu Panupong Pasupat and Mehran Kazemi. 2024. In-context Learning with Retrieved Demonstrations for Language Models: A Survey. arxiv: 2401.11624 [cs.CL]"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331317"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3005536"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2889473"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10791-019-09353-0"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.genbench-1.14"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"crossref","unstructured":"Sheshera Mysore Andrew McCallum and Hamed Zamani. 2023. Large Language Model Augmented Narrative Driven Recommendations. arxiv: 2306.02250 [cs.IR]","DOI":"10.1145\/3604915.3608829"},{"key":"e_1_3_2_1_54_1","unstructured":"Jianmo Ni Chen Qu Jing Lu Zhuyun Dai Gustavo Hern\u00e1ndez \u00c1brego Ji Ma Vincent Y. Zhao Yi Luan Keith B. Hall Ming-Wei Chang and Yinfei Yang. 2021. Large Dual Encoders Are Generalizable Retrievers. arxiv: 2112.07899 [cs.IR]"},{"key":"e_1_3_2_1_55_1","volume-title":"Passage Re-ranking with BERT. CoRR","author":"Nogueira Rodrigo Frassetto","year":"2019","unstructured":"Rodrigo Frassetto Nogueira and Kyunghyun Cho. 2019. Passage Re-ranking with BERT. CoRR , Vol. abs\/1901.04085 (2019). showeprint[arXiv]1901.04085 http:\/\/arxiv.org\/abs\/1901.04085"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/2020.FINDINGS-EMNLP.63"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462830"},{"key":"e_1_3_2_1_58_1","unstructured":"OpenAI. 2023. GPT-4 Technical Report. arxiv: 2303.08774 [cs.CL]"},{"key":"e_1_3_2_1_59_1","volume-title":"Oh (Eds.)","volume":"35","author":"Ouyang Long","year":"2022","unstructured":"Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F Christiano, Jan Leike, and Ryan Lowe. 2022. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35. Curran Associates, Inc., 27730--27744. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2022\/file\/b1efde53be364a73914f58805a001731-Paper-Conference.pdf"},{"key":"e_1_3_2_1_60_1","volume-title":"Paulsen and Mrinal Raghupathi","author":"Vern","year":"2016","unstructured":"Vern I. Paulsen and Mrinal Raghupathi. 2016. An Introduction to the Theory of Reproducing Kernel Hilbert Spaces. Cambridge University Press."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"crossref","unstructured":"Ronak Pradeep Kai Hui Jai Gupta Adam D. Lelkes Honglei Zhuang Jimmy Lin Donald Metzler and Vinh Q. Tran. 2023 a. How Does Generative Retrieval Scale to Millions of Passages?arxiv: 2305.11841 [cs.IR]","DOI":"10.18653\/v1\/2023.emnlp-main.83"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2312.02724"},{"key":"e_1_3_2_1_63_1","unstructured":"Alec Radford Jeffrey Wu Rewon Child David Luan Dario Amodei Ilya Sutskever et al. 2019. Language models are unsupervised multitask learners. OpenAI blog Vol. 1 8 (2019) 9."},{"key":"e_1_3_2_1_64_1","volume-title":"Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arxiv","author":"Reimers Nils","year":"1908","unstructured":"Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arxiv: 1908.10084 [cs.CL]"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3409256.3409821"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2018.10.009"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.naacl-main.191"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772780"},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.eacl-main.20"},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"crossref","unstructured":"Procheta Sen Sourav Saha Debasis Ganguly Manisha Verma and Dwaipayan Roy. 2022. Measuring and Comparing the Consistency of IR Models for Query Pairs with Similar and Different Information Needs. In CIKM. ACM 4449--4453.","DOI":"10.1145\/3511808.3557637"},{"key":"e_1_3_2_1_71_1","volume-title":"LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval. In The Eleventh International Conference on Learning Representations, ICLR 2023","author":"Shen Tao","year":"2023","unstructured":"Tao Shen, Xiubo Geng, Chongyang Tao, Can Xu, Xiaolong Huang, Binxing Jiao, Linjun Yang, and Daxin Jiang. 2023. LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1--5, 2023. OpenReview.net. https:\/\/openreview.net\/pdf?id=PfpEtB3-csK"},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/2180868.2180873"},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3592082"},{"key":"e_1_3_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D13-1170"},{"key":"e_1_3_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.60"},{"key":"e_1_3_2_1_76_1","unstructured":"Jianlin Su Yu Lu Shengfeng Pan Ahmed Murtadha Bo Wen and Yunfeng Liu. 2023. RoFormer: Enhanced Transformer with Rotary Position Embedding. arxiv: 2104.09864 [cs.CL]"},{"key":"e_1_3_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.923"},{"key":"e_1_3_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.newsum-1.6"},{"key":"e_1_3_2_1_79_1","unstructured":"Hugo Touvron Louis Martin Kevin Stone Peter Albert Amjad Almahairi Yasmine Babaei Nikolay Bashlykov Soumya Batra Prajjwal Bhargava Shruti Bhosale Dan Bikel Lukas Blecher Cristian Canton Ferrer Moya Chen Guillem Cucurull David Esiobu Jude Fernandes Jeremy Fu Wenyin Fu Brian Fuller Cynthia Gao Vedanuj Goswami Naman Goyal Anthony Hartshorn Saghar Hosseini Rui Hou Hakan Inan Marcin Kardas Viktor Kerkez Madian Khabsa Isabel Kloumann Artem Korenev Punit Singh Koura Marie-Anne Lachaux Thibaut Lavril Jenya Lee Diana Liskovich Yinghai Lu Yuning Mao Xavier Martinet Todor Mihaylov Pushkar Mishra Igor Molybog Yixin Nie Andrew Poulton Jeremy Reizenstein Rashi Rungta Kalyan Saladi Alan Schelten Ruan Silva Eric Michael Smith Ranjan Subramanian Xiaoqing Ellen Tan Binh Tang Ross Taylor Adina Williams Jian Xiang Kuan Puxin Xu Zheng Yan Iliyan Zarov Yuchen Zhang Angela Fan Melanie Kambadur Sharan Narang Aurelien Rodriguez Robert Stojnic Sergey Edunov and Thomas Scialom. 2023. Llama 2: Open Foundation and Fine-Tuned Chat Models. arxiv: 2307.09288 [cs.CL]"},{"key":"e_1_3_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-45442-5_52"},{"key":"e_1_3_2_1_81_1","unstructured":"Ben Wang. 2021. Mesh-Transformer-JAX: Model-Parallel Implementation of Transformer Language Model with JAX. https:\/\/github.com\/kingoflolz\/mesh-transformer-jax."},{"key":"e_1_3_2_1_82_1","volume-title":"GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model","author":"Wang Ben","year":"2021","unstructured":"Ben Wang and Aran Komatsuzaki. 2022. GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model, 2021."},{"key":"e_1_3_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.585"},{"key":"e_1_3_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/2022.EMNLP-MAIN.35"},{"key":"e_1_3_2_1_85_1","volume-title":"Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval. In 9th International Conference on Learning Representations, ICLR 2021","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 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3--7, 2021. OpenReview.net. https:\/\/openreview.net\/forum?id=zeFrfgyZln"},{"key":"e_1_3_2_1_86_1","volume-title":"Proc. of SIGIR '19","author":"Zendel Oleg","unstructured":"Oleg Zendel, Anna Shtok, Fiana Raiber, Oren Kurland, and J. Shane Culpepper. 2019. Information Needs, Queries, and Query Performance Prediction. In Proc. of SIGIR '19. Association for Computing Machinery, New York, NY, USA, 395--404."},{"key":"e_1_3_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.1145\/2990508"},{"key":"e_1_3_2_1_88_1","volume-title":"Proc. 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '07)","author":"Zhou Yun","unstructured":"Yun Zhou and W. Bruce Croft. 2007. Query Performance Prediction in Web Search Environments. In Proc. 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '07). Association for Computing Machinery, New York, NY, USA, 543--550."}],"event":{"name":"SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Washington DC USA","acronym":"SIGIR 2024","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3626772.3657842","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3626772.3657842","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T05:41:41Z","timestamp":1755841301000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3626772.3657842"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,10]]},"references-count":88,"alternative-id":["10.1145\/3626772.3657842","10.1145\/3626772"],"URL":"https:\/\/doi.org\/10.1145\/3626772.3657842","relation":{},"subject":[],"published":{"date-parts":[[2024,7,10]]},"assertion":[{"value":"2024-07-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}