{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T05:23:10Z","timestamp":1784092990773,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":66,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T00:00:00Z","timestamp":1689638400000},"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":[[2023,7,19]]},"DOI":"10.1145\/3539618.3592067","type":"proceedings-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T00:22:23Z","timestamp":1689726143000},"page":"2409-2414","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["ExaRanker: Synthetic Explanations Improve Neural Rankers"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4674-8705","authenticated-orcid":false,"given":"Fernando","family":"Ferraretto","sequence":"first","affiliation":[{"name":"UNICAMP, Campinas, UNK, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7205-2094","authenticated-orcid":false,"given":"Thiago","family":"Laitz","sequence":"additional","affiliation":[{"name":"UNICAMP, Campinas, UNK, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5652-0852","authenticated-orcid":false,"given":"Roberto","family":"Lotufo","sequence":"additional","affiliation":[{"name":"UNICAMP, Campinas, UNK, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2600-6035","authenticated-orcid":false,"given":"Rodrigo","family":"Nogueira","sequence":"additional","affiliation":[{"name":"UNICAMP, Campinas, UNK, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,7,18]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"MS MARCO: A Human Generated MAchine Reading COmprehension Dataset. arXiv:1611.09268v3","author":"Bajaj P.","year":"2018","unstructured":"P. Bajaj, D. Campos, N. Craswell, L. Deng, J. Gao, X. Liu, R. Majumder, A. Mc- Namara, B. Mitra, T. Nguyen, M. Rosenberg, X. Song, A. Stoica, S. Tiwary, and T. Wang. MS MARCO: A Human Generated MAchine Reading COmprehension Dataset. arXiv:1611.09268v3, 2018."},{"key":"e_1_3_2_2_2_1","volume-title":"Efficient index-based snippet generation. ACM Transactions on Information Systems (TOIS), 32(2):1--24","author":"Bast H.","year":"2014","unstructured":"H. Bast and M. Celikik. Efficient index-based snippet generation. ACM Transactions on Information Systems (TOIS), 32(2):1--24, 2014."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531863"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-30671-1_58"},{"key":"e_1_3_2_2_5_1","volume-title":"Induced natural language rationales and interleaved markup tokens enable extrapolation in large language models. arXiv preprint arXiv:2208.11445","author":"Bueno M.","year":"2022","unstructured":"M. Bueno, C. Gemmel, J. Dalton, R. Lotufo, and R. Nogueira. Induced natural language rationales and interleaved markup tokens enable extrapolation in large language models. arXiv preprint arXiv:2208.11445, 2022."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380206"},{"key":"e_1_3_2_2_7_1","volume-title":"Overview of the TREC 2020 deep learning track. CoRR, abs\/2102","author":"Craswell N.","year":"2021","unstructured":"N. Craswell, B. Mitra, E. Yilmaz, and D. Campos. Overview of the TREC 2020 deep learning track. CoRR, abs\/2102.07662, 2021."},{"key":"e_1_3_2_2_8_1","volume-title":"Promptagator: Few-shot dense retrieval from 8 examples. arXiv preprint arXiv:2209.11755","author":"Dai Z.","year":"2022","unstructured":"Z. Dai, V. Y. Zhao, J. Ma, Y. Luan, J. Ni, J. Lu, A. Bakalov, K. Guu, K. B. Hall, and M.-W. Chang. Promptagator: Few-shot dense retrieval from 8 examples. arXiv preprint arXiv:2209.11755, 2022."},{"key":"e_1_3_2_2_9_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 J.","year":"2019","unstructured":"J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. 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), pages 4171--4186, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics."},{"key":"e_1_3_2_2_10_1","volume-title":"Learning to scaffold: Optimizing model explanations for teaching. arXiv preprint arXiv:2204.10810","author":"Fernandes P.","year":"2022","unstructured":"P. Fernandes, M. Treviso, D. Pruthi, A. F. Martins, and G. Neubig. Learning to scaffold: Optimizing model explanations for teaching. arXiv preprint arXiv:2204.10810, 2022."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331312"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463098"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.203"},{"key":"e_1_3_2_2_14_1","volume-title":"Rethink training of bert rerankers in multi-stage retrieval pipeline. arXiv preprint arXiv:2101.08751","author":"Gao L.","year":"2021","unstructured":"L. Gao, Z. Dai, and J. Callan. Rethink training of bert rerankers in multi-stage retrieval pipeline. arXiv preprint arXiv:2101.08751, 2021."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080751"},{"key":"e_1_3_2_2_16_1","volume-title":"Introducing neural bag of whole-words with colberter: Contextualized late interactions using enhanced reduction. arXiv preprint arXiv:2203.13088","author":"Hofst\u00e4tter S.","year":"2022","unstructured":"S. Hofst\u00e4tter, O. Khattab, S. Althammer, M. Sertkan, and A. Hanbury. Introducing neural bag of whole-words with colberter: Contextualized late interactions using enhanced reduction. arXiv preprint arXiv:2203.13088, 2022."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462891"},{"key":"e_1_3_2_2_18_1","volume-title":"Large language models can self-improve. arXiv preprint arXiv:2210.11610","author":"Huang J.","year":"2022","unstructured":"J. Huang, S. S. Gu, L. Hou, Y. Wu, X. Wang, H. Yu, and J. Han. Large language models can self-improve. arXiv preprint arXiv:2210.11610, 2022."},{"key":"e_1_3_2_2_19_1","volume-title":"Unsupervised dense information retrieval with contrastive learning","author":"Izacard G.","year":"2021","unstructured":"G. Izacard, M. Caron, L. Hosseini, S. Riedel, P. Bojanowski, A. Joulin, and E. Grave. Unsupervised dense information retrieval with contrastive learning, 2021."},{"key":"e_1_3_2_2_20_1","volume-title":"Inpars-v2: Large language models as efficient dataset generators for information retrieval","author":"Jeronymo V.","year":"2023","unstructured":"V. Jeronymo, L. Bonifacio, H. Abonizio, M. Fadaee, R. Lotufo, J. Zavrel, and R. Nogueira. Inpars-v2: Large language models as efficient dataset generators for information retrieval, 2023."},{"key":"e_1_3_2_2_21_1","volume-title":"Inferring implicit relations with language models. arXiv preprint arXiv:2204.13778","author":"Katz U.","year":"2022","unstructured":"U. Katz, M. Geva, and J. Berant. Inferring implicit relations with language models. arXiv preprint arXiv:2204.13778, 2022."},{"key":"e_1_3_2_2_22_1","volume-title":"Parade: Passage representation aggregation for document reranking. arXiv preprint arXiv:2008.09093","author":"Li C.","year":"2020","unstructured":"C. Li, A. Yates, S. MacAvaney, B. He, and Y. Sun. Parade: Passage representation aggregation for document reranking. arXiv preprint arXiv:2008.09093, 2020."},{"key":"e_1_3_2_2_23_1","volume-title":"Pyserini: An easy-to-use Python toolkit to support replicable ir research with sparse and dense representations. ArXiv, abs\/2102.10073","author":"Lin J.","year":"2021","unstructured":"J. Lin, X. Ma, S.-C. Lin, J.-H. Yang, R. Pradeep, and R. Nogueira. Pyserini: An easy-to-use Python toolkit to support replicable ir research with sparse and dense representations. ArXiv, abs\/2102.10073, 2021."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-02181-7"},{"key":"e_1_3_2_2_25_1","volume-title":"International Conference on Learning Representations","author":"Loshchilov I.","year":"2019","unstructured":"I. Loshchilov and F. Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2019."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.220"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331317"},{"key":"e_1_3_2_2_28_1","volume-title":"Teaching small language models to reason","author":"Magister L. C.","year":"2022","unstructured":"L. C. Magister, J. Mallinson, J. Adamek, E. Malmi, and A. Severyn. Teaching small language models to reason, 2022."},{"key":"e_1_3_2_2_29_1","volume-title":"Companion Proceedings of the The Web Conference 2018, WWW '18, page 1941--1942, Republic and Canton of Geneva, CHE, 2018. International World Wide Web Conferences Steering Committee.","author":"Maia M.","unstructured":"M. Maia, S. Handschuh, A. Freitas, B. Davis, R. McDermott, M. Zarrouk, and A. Balahur. Www'18 open challenge: Financial opinion mining and question answering. In Companion Proceedings of the The Web Conference 2018, WWW '18, page 1941--1942, Republic and Canton of Geneva, CHE, 2018. International World Wide Web Conferences Steering Committee."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.naacl-main.331"},{"key":"e_1_3_2_2_31_1","volume-title":"Passage re-ranking with bert. arXiv preprint arXiv:1901.04085","author":"Nogueira R.","year":"2019","unstructured":"R. Nogueira and K. Cho. Passage re-ranking with bert. arXiv preprint arXiv:1901.04085, 2019."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.63"},{"key":"e_1_3_2_2_33_1","volume-title":"Deep Learning for Code Workshop","author":"Nye M.","year":"2022","unstructured":"M. Nye, A. J. Andreassen, G. Gur-Ari, H. Michalewski, J. Austin, D. Bieber, D. Dohan, A. Lewkowycz, M. Bosma, D. Luan, C. Sutton, and A. Odena. Show your work: Scratchpads for intermediate computation with language models. In Deep Learning for Code Workshop, 2022."},{"key":"e_1_3_2_2_34_1","volume-title":"Training language models to follow instructions with human feedback. arXiv preprint arXiv:2203.02155","author":"Ouyang L.","year":"2022","unstructured":"L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al. Training language models to follow instructions with human feedback. arXiv preprint arXiv:2203.02155, 2022."},{"issue":"140","key":"e_1_3_2_2_35_1","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel C.","year":"2020","unstructured":"C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, P. J. Liu, et al. Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140):1--67, 2020.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_2_36_1","volume-title":"Explaining documents' relevance to search queries. arXiv preprint arXiv:2111.01314","author":"Rahimi R.","year":"2021","unstructured":"R. Rahimi, Y. Kim, H. Zamani, and J. Allan. Explaining documents' relevance to search queries. arXiv preprint arXiv:2111.01314, 2021."},{"key":"e_1_3_2_2_37_1","volume-title":"arXiv preprint arXiv:2109.02102","author":"Recchia G.","year":"2021","unstructured":"G. Recchia. Teaching autoregressive language models complex tasks by demonstration. arXiv preprint arXiv:2109.02102, 2021."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocaa091"},{"key":"e_1_3_2_2_39_1","first-page":"109","article-title":"Okapi at trec-3","volume":"109","author":"Robertson S. E.","year":"1995","unstructured":"S. E. Robertson, S. Walker, S. Jones, M. M. Hancock-Beaulieu, M. Gatford, et al. Okapi at trec-3. Nist Special Publication Sp, 109:109, 1995.","journal-title":"Nist Special Publication Sp"},{"key":"e_1_3_2_2_40_1","volume-title":"defense of cross-encoders for zero-shot retrieval. arXiv preprint arXiv:2212.06121","author":"Rosa G.","year":"2022","unstructured":"G. Rosa, L. Bonifacio, V. Jeronymo, H. Abonizio, M. Fadaee, R. Lotufo, and R. Nogueira. In defense of cross-encoders for zero-shot retrieval. arXiv preprint arXiv:2212.06121, 2022."},{"key":"e_1_3_2_2_41_1","volume-title":"No parameter left behind: How distillation and model size affect zero-shot retrieval. arXiv preprint arXiv:2206.02873","author":"Rosa G. M.","year":"2022","unstructured":"G. M. Rosa, L. Bonifacio, V. Jeronymo, H. Abonizio, M. Fadaee, R. Lotufo, and R. Nogueira. No parameter left behind: How distillation and model size affect zero-shot retrieval. arXiv preprint arXiv:2206.02873, 2022."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357859"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.249"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.naacl-main.272"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401286"},{"key":"e_1_3_2_2_46_1","volume-title":"Interpreting search result rankings through intent modeling. arXiv preprint arXiv:1809.05190","author":"Singh J.","year":"2018","unstructured":"J. Singh and A. Anand. Interpreting search result rankings through intent modeling. arXiv preprint arXiv:1809.05190, 2018."},{"key":"e_1_3_2_2_47_1","volume-title":"Posthoc interpretability of learning to rank models using secondary training data. arXiv preprint arXiv:1806.11330","author":"Singh J.","year":"2018","unstructured":"J. Singh and A. Anand. Posthoc interpretability of learning to rank models using secondary training data. arXiv preprint arXiv:1806.11330, 2018."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290620"},{"key":"e_1_3_2_2_49_1","volume-title":"Valid explanations for learning to rank models. arXiv preprint arXiv:2004.13972","author":"Singh J.","year":"2020","unstructured":"J. Singh, Z. Wang, M. Khosla, and A. Anand. Valid explanations for learning to rank models. arXiv preprint arXiv:2004.13972, 2020."},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"crossref","unstructured":"I. Soboroff S. Huang and D. Harman. Trec 2018 news track overview.","DOI":"10.6028\/NIST.SP.500-331.news-overview"},{"key":"e_1_3_2_2_51_1","volume-title":"Domain adaptation for memory-efficient dense retrieval. arXiv preprint arXiv:2205.11498","author":"Thakur N.","year":"2022","unstructured":"N. Thakur, N. Reimers, and J. Lin. Domain adaptation for memory-efficient dense retrieval. arXiv preprint arXiv:2205.11498, 2022."},{"key":"e_1_3_2_2_52_1","volume-title":"Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)","author":"Thakur N.","year":"2021","unstructured":"N. Thakur, N. Reimers, A. R\u00fcckl\u00e9, A. Srivastava, and I. Gurevych. Beir: A heterogeneous benchmark for zero-shot evaluation of information retrieval models. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2), 2021."},{"key":"e_1_3_2_2_53_1","volume-title":"Investigating searchers' mental models to inform search explanations. ACM Transactions on Information Systems (TOIS), 38(1):1--25","author":"Thomas P.","year":"2019","unstructured":"P. Thomas, B. Billerbeck, N. Craswell, and R. W. White. Investigating searchers' mental models to inform search explanations. ACM Transactions on Information Systems (TOIS), 38(1):1--25, 2019."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/290941.290947"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/1277741.1277766"},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331377"},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3471158.3472256"},{"key":"e_1_3_2_2_58_1","volume-title":"Overview of the trec 2004 robust retrieval track","author":"Voorhees E.","year":"2005","unstructured":"E. Voorhees. Overview of the trec 2004 robust retrieval track, 2005-08-01 2005."},{"key":"e_1_3_2_2_59_1","volume-title":"Gpl: Generative pseudo labeling for unsupervised domain adaptation of dense retrieval. arXiv preprint arXiv:2112.07577","author":"Wang K.","year":"2021","unstructured":"K. Wang, N. Thakur, N. Reimers, and I. Gurevych. Gpl: Generative pseudo labeling for unsupervised domain adaptation of dense retrieval. arXiv preprint arXiv:2112.07577, 2021."},{"key":"e_1_3_2_2_60_1","volume-title":"Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171","author":"Wang X.","year":"2022","unstructured":"X. Wang, J. Wei, D. Schuurmans, Q. Le, E. Chi, and D. Zhou. Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171, 2022."},{"key":"e_1_3_2_2_61_1","volume-title":"Zero-shot dense retrieval with momentum adversarial domain invariant representations. arXiv preprint arXiv:2110.07581","author":"Xin J.","year":"2021","unstructured":"J. Xin, C. Xiong, A. Srinivasan, A. Sharma, D. Jose, and P. N. Bennett. Zero-shot dense retrieval with momentum adversarial domain invariant representations. arXiv preprint arXiv:2110.07581, 2021."},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532067"},{"key":"e_1_3_2_2_63_1","volume-title":"Star: Bootstrapping reasoning with reasoning. arXiv preprint arXiv:2203.14465","author":"Zelikman E.","year":"2022","unstructured":"E. Zelikman, Y. Wu, and N. D. Goodman. Star: Bootstrapping reasoning with reasoning. arXiv preprint arXiv:2203.14465, 2022."},{"key":"e_1_3_2_2_64_1","volume-title":"Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625","author":"Zhou D.","year":"2022","unstructured":"D. Zhou, N. Sch\u00e4rli, L. Hou, J. Wei, N. Scales, X. Wang, D. Schuurmans, O. Bousquet, Q. Le, and E. Chi. Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625, 2022."},{"key":"e_1_3_2_2_65_1","volume-title":"Rankt5: Fine-tuning t5 for text ranking with ranking losses. arXiv preprint arXiv:2210.10634","author":"Zhuang H.","year":"2022","unstructured":"H. Zhuang, Z. Qin, R. Jagerman, K. Hui, J. Ma, J. Lu, J. Ni, X.Wang, and M. Bendersky. Rankt5: Fine-tuning t5 for text ranking with ranking losses. arXiv preprint arXiv:2210.10634, 2022."},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441796"}],"event":{"name":"SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Taipei Taiwan","acronym":"SIGIR '23","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3539618.3592067","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3539618.3592067","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:38:03Z","timestamp":1750178283000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3539618.3592067"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,18]]},"references-count":66,"alternative-id":["10.1145\/3539618.3592067","10.1145\/3539618"],"URL":"https:\/\/doi.org\/10.1145\/3539618.3592067","relation":{},"subject":[],"published":{"date-parts":[[2023,7,18]]},"assertion":[{"value":"2023-07-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}