{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T02:26:03Z","timestamp":1783736763877,"version":"3.55.0"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032212887","type":"print"},{"value":"9783032212894","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-21289-4_29","type":"book-chapter","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T01:06:12Z","timestamp":1774314372000},"page":"450-465","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Revealing MonoT5s Learning Mechanisms via\u00a0Prompt-Token Adaptation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7619-8399","authenticated-orcid":false,"given":"Marco","family":"Braga","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8914-2659","authenticated-orcid":false,"given":"Sean","family":"MacAvaney","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3143-279X","authenticated-orcid":false,"given":"Craig","family":"Macdonald","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6080-8170","authenticated-orcid":false,"given":"Gabriella","family":"Pasi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"29_CR1","unstructured":"Barbero, F., et al.: Why do LLMs attend to the first token? In: Second Conference on Language Modeling (2025)"},{"key":"29_CR2","doi-asserted-by":"publisher","unstructured":"Boteva, V., Gholipour, D., Sokolov, A., Riezler, S.: A full-text learning to rank dataset for medical information retrieval. In: European Conference on Information Retrieval, pp. 716\u2013722. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-30671-1_58","DOI":"10.1007\/978-3-319-30671-1_58"},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Braga, M.: Personalized large language models through parameter efficient fine-tuning techniques. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3076\u20133076 (2024)","DOI":"10.1145\/3626772.3657657"},{"key":"29_CR4","doi-asserted-by":"crossref","unstructured":"Braga, M., Kasela, P., Raganato, A., Pasi, G.: Investigating task arithmetic for zero-shot information retrieval. In: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025, pp. 2738\u20132743. Association for Computing Machinery, New York (2025)","DOI":"10.1145\/3726302.3730216"},{"key":"29_CR5","first-page":"3619","volume":"14","author":"T Cai","year":"2013","unstructured":"Cai, T., Zhou, W.X.: A max-norm constrained minimization approach to 1-bit matrix completion. J. Mach. Learn. Res. 14, 3619\u20133647 (2013)","journal-title":"J. Mach. Learn. Res."},{"key":"29_CR6","doi-asserted-by":"crossref","unstructured":"Cer, D., Diab, M., Agirre, E., Lopez-Gazpio, I., Specia, L.: SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation. In: Bethard, S., Carpuat, M., Apidianaki, M., Mohammad, S.M., Cer, D., Jurgens, D. (eds.) Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 1\u201314. Association for Computational Linguistics, Vancouver, Canada (2017)","DOI":"10.18653\/v1\/S17-2001"},{"key":"29_CR7","doi-asserted-by":"publisher","unstructured":"Chang, X., Mishra, D., Macdonald, C., MacAvaney, S.: Neural passage quality estimation for static pruning. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR 2024, pp. 174\u2013185. Association for Computing Machinery, New York (2024). https:\/\/doi.org\/10.1145\/3626772.3657765","DOI":"10.1145\/3626772.3657765"},{"key":"29_CR8","doi-asserted-by":"crossref","unstructured":"Cohan, A., Feldman, S., Beltagy, I., Downey, D., Weld, D.: SPECTER: document-level representation learning using citation-informed transformers. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J. (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 2270\u20132282. Association for Computational Linguistics, Online (2020). https:\/\/aclanthology.org\/2020.acl-main.207\/","DOI":"10.18653\/v1\/2020.acl-main.207"},{"key":"29_CR9","doi-asserted-by":"crossref","unstructured":"Craswell, N., Mitra, B., Yilmaz, E., Campos, D.: Overview of the trec 2020 deep learning track (2021). https:\/\/arxiv.org\/abs\/2102.07662","DOI":"10.6028\/NIST.SP.1266.deep-overview"},{"key":"29_CR10","doi-asserted-by":"crossref","unstructured":"Craswell, N., Mitra, B., Yilmaz, E., Campos, D., Voorhees, E.M.: Overview of the trec 2019 deep learning track (2020). https:\/\/arxiv.org\/abs\/2003.07820","DOI":"10.6028\/NIST.SP.1266.deep-overview"},{"key":"29_CR11","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1080\/01621459.1961.10482090","volume":"56","author":"OJ Dunn","year":"1961","unstructured":"Dunn, O.J.: Multiple comparisons among means. J. Am. Stat. Assoc. 56, 52\u201364 (1961)","journal-title":"J. Am. Stat. Assoc."},{"key":"29_CR12","unstructured":"Gu, X., et al.: When attention sink emerges in language models: an empirical view. In: The Thirteenth International Conference on Learning Representations (2025)"},{"key":"29_CR13","unstructured":"Herzog, R., K\u00f6hne, F., Kreis, L., Schiela, A.: Frobenius-type norms and inner products of matrices and linear maps with applications to neural network training. arXiv preprint arXiv:2311.15419 (2023)"},{"issue":"2","key":"29_CR14","first-page":"3","volume":"1","author":"EJ Hu","year":"2022","unstructured":"Hu, E.J., et al.: Lora: low-rank adaptation of large language models. ICLR 1(2), 3 (2022)","journal-title":"ICLR"},{"key":"29_CR15","unstructured":"Humeau, S., Shuster, K., Lachaux, M.A., Weston, J.: Poly-encoders: transformer architectures and pre-training strategies for fast and accurate multi-sentence scoring. arXiv preprint arXiv:1905.01969 (2019)"},{"key":"29_CR16","unstructured":"Ilharco, G., Ribeiro, M.T., Wortsman, M., Schmidt, L., Hajishirzi, H., Farhadi, A.: Editing models with task arithmetic. In: The Eleventh International Conference on Learning Representations (2022)"},{"key":"29_CR17","unstructured":"Lin, S.C., Yang, J.H., Lin, J.: Distilling dense representations for ranking using tightly-coupled teachers. arXiv preprint arXiv:2010.11386 (2020)"},{"key":"29_CR18","doi-asserted-by":"publisher","unstructured":"MacAvaney, S., Yates, A., Feldman, S., Downey, D., Cohan, A., Goharian, N.: Simplified data wrangling with ir_datasets. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR 2021, pp. 2429\u20132436. Association for Computing Machinery (2021). https:\/\/doi.org\/10.1145\/3404835.3463254","DOI":"10.1145\/3404835.3463254"},{"key":"29_CR19","doi-asserted-by":"crossref","unstructured":"Mackie, I., Dalton, J., Yates, A.: How deep is your learning: the dl-hard annotated deep learning dataset. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (2021)","DOI":"10.1145\/3404835.3463262"},{"key":"29_CR20","doi-asserted-by":"crossref","unstructured":"Maia, M., et al.: Www\u201918 open challenge: financial opinion mining and question answering. In: Companion Proceedings of the the Web Conference 2018, pp. 1941\u20131942 (2018)","DOI":"10.1145\/3184558.3192301"},{"key":"29_CR21","unstructured":"Nguyen, T., et al.: MS MARCO: a human generated machine reading comprehension dataset. In: Besold, T.R., Bordes, A., d\u2019Avila Garcez, A.S., Wayne, G. (eds.) 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 2016), Barcelona, Spain, December 9, 2016. CEUR Workshop Proceedings, vol.\u00a01773. CEUR-WS.org (2016)"},{"key":"29_CR22","doi-asserted-by":"crossref","unstructured":"Nogueira, R., Jiang, Z., Pradeep, R., Lin, J.: Document ranking with a pretrained sequence-to-sequence model. In: Cohn, T., He, Y., Liu, Y. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 708\u2013718. Association for Computational Linguistics, Online (2020). https:\/\/aclanthology.org\/2020.findings-emnlp.63\/","DOI":"10.18653\/v1\/2020.findings-emnlp.63"},{"key":"29_CR23","unstructured":"Nogueira, R., Yang, W., Cho, K., Lin, J.: Multi-stage document ranking with bert. arXiv preprint arXiv:1910.14424 (2019)"},{"key":"29_CR24","doi-asserted-by":"crossref","unstructured":"Parry, A., Fr\u00f6be, M., MacAvaney, S., Potthast, M., Hagen, M.: Analyzing adversarial attacks on sequence-to-sequence relevance models. In: European Conference on Information Retrieval, pp. 286\u2013302. Springer, Cham (2024)","DOI":"10.1007\/978-3-031-56060-6_19"},{"issue":"140","key":"29_CR25","first-page":"1","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(140), 1\u201367 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"29_CR26","doi-asserted-by":"crossref","unstructured":"Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: SQuAD: 100,000+ questions for machine comprehension of text. In: Su, J., Duh, K., Carreras, X. (eds.) Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2383\u20132392. Association for Computational Linguistics, Austin, Texas (2016). https:\/\/aclanthology.org\/D16-1264\/","DOI":"10.18653\/v1\/D16-1264"},{"issue":"6","key":"29_CR27","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1007\/BF01068419","volume":"15","author":"DJ Schuirmann","year":"1987","unstructured":"Schuirmann, D.J.: A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability. J. Pharmacokinet. Biopharm. 15(6), 657\u2013680 (1987)","journal-title":"J. Pharmacokinet. Biopharm."},{"key":"29_CR28","unstructured":"Thakur, N., Reimers, N., R\u00fcckl\u00e9, A., Srivastava, A., Gurevych, I.: 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":"29_CR29","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"29_CR30","doi-asserted-by":"crossref","unstructured":"Voorhees, E., et al.: Trec-covid: constructing a pandemic information retrieval test collection. In: ACM SIGIR Forum, vol.\u00a054, pp. 1\u201312. ACM New York (2021)","DOI":"10.1145\/3451964.3451965"},{"key":"29_CR31","doi-asserted-by":"crossref","unstructured":"Wadden, D., et al.: Fact or fiction: verifying scientific claims. In: Webber, B., Cohn, T., He, Y., Liu, Y. (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 7534\u20137550. Association for Computational Linguistics, Online (2020). https:\/\/aclanthology.org\/2020.emnlp-main.609\/","DOI":"10.18653\/v1\/2020.emnlp-main.609"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-21289-4_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T12:15:16Z","timestamp":1779279316000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-21289-4_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032212887","9783032212894"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-21289-4_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"25 March 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Delft","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 March 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 April 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"48","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2026.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}