{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T06:49:02Z","timestamp":1772261342625,"version":"3.50.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031887192","type":"print"},{"value":"9783031887208","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-88720-8_21","type":"book-chapter","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T12:02:01Z","timestamp":1743768121000},"page":"123-129","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Jina Embeddings V3: Multilingual Text Encoder with\u00a0Low-Rank Adaptations"],"prefix":"10.1007","author":[{"given":"Saba","family":"Sturua","sequence":"first","affiliation":[]},{"given":"Isabelle","family":"Mohr","sequence":"additional","affiliation":[]},{"given":"Mohammad","family":"Kalim Akram","sequence":"additional","affiliation":[]},{"given":"Michael","family":"G\u00fcnther","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Markus","family":"Krimmel","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Georgios","family":"Mastrapas","sequence":"additional","affiliation":[]},{"given":"Andreas","family":"Koukounas","sequence":"additional","affiliation":[]},{"given":"Nan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Han","family":"Xiao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,3]]},"reference":[{"key":"21_CR1","unstructured":"Bajaj, P., et\u00a0al.: MS MARCO: A Human Generated MAchine Reading COmprehension Dataset. arXiv:1611.09268 (2016)"},{"key":"21_CR2","doi-asserted-by":"publisher","unstructured":"Conneau, A., et al.: Unsupervised Cross-lingual Representation Learning at Scale. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J.R. (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, 5-10 July 2020, pp. 8440\u20138451 (2020). https:\/\/doi.org\/10.18653\/V1\/2020.ACL-MAIN.747","DOI":"10.18653\/V1\/2020.ACL-MAIN.747"},{"key":"21_CR3","unstructured":"Dao, T.: FlashAttention-2: faster attention with better parallelism and work partitioning. In: ICLR 2024: The Twelfth International Conference on Learning Representations (2024)"},{"key":"21_CR4","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) 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), pp. 4171\u20134186 (2019). https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"21_CR5","doi-asserted-by":"crossref","unstructured":"Gao, T., Yao, X., Chen, D.: SimCSE: simple contrastive learning of sentence embeddings. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 6894\u20136910. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"21_CR6","doi-asserted-by":"crossref","unstructured":"G\u00fcnther, M., Milliken, L., Geuter, J., Mastrapas, G., Wang, B., Xiao, H.: Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models. arXiv:2307.11224 (2023)","DOI":"10.18653\/v1\/2023.nlposs-1.2"},{"key":"21_CR7","unstructured":"G\u00fcnther, M., et\u00a0al.: Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents. arXiv:2310.19923 (2023)"},{"key":"21_CR8","unstructured":"Hu, E.J., et al.: LoRA: low-Rank Adaptation of Large Language Models. arXiv:2106.09685v2 (2021)"},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Jiang, T., Huang, S., Luan, Z., Wang, D., Zhuang, F.: Scaling Sentence Embeddings with Large Language Models. arXiv:2307.16645 (2024)","DOI":"10.18653\/v1\/2024.findings-emnlp.181"},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Jiao, X., et al.: TinyBERT: distilling BERT for Natural language understanding. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 4163\u20134174. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.372"},{"key":"21_CR11","unstructured":"K\u00f6pf, A., et\u00a0al.: OpenAssistant conversations \u2013 democratizing large language model alignment. Adv. Neural Inform. Process. Syst. 36 (2024)"},{"key":"21_CR12","unstructured":"Kusupati, A., et al.: Matryoshka Representation Learning. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems, vol.\u00a035, pp. 30233\u201330249 (2022)"},{"key":"21_CR13","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1162\/tacl_a_00276","volume":"7","author":"T Kwiatkowski","year":"2019","unstructured":"Kwiatkowski, T., et al.: Natural questions: a benchmark for question answering research. Trans. Associat. Comput. Linguist. 7, 453\u2013466 (2019). https:\/\/doi.org\/10.1162\/tacl_a_00276","journal-title":"Trans. Associat. Comput. Linguist."},{"key":"21_CR14","unstructured":"Lewis, P., et al. Retrieval-augmented generation for knowledge-intensive NLP tasks. Adv. Neural. Inf. Process. Syst. 33, 9459\u20139474 (2020)"},{"key":"21_CR15","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized bert pretraining approach. arXiv:1907.11692 (2019)"},{"key":"21_CR16","unstructured":"Mohr, I., Krimmel, M., et\u00a0al.: Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings. arXiv:2402.17016 (2024)"},{"key":"21_CR17","doi-asserted-by":"publisher","unstructured":"Muennighoff, N., Tazi, N., Magne, L., Reimers, N.: MTEB: massive text embedding benchmark. In: Vlachos, A., Augenstein, I. (eds.) Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023, pp. 2006\u20132029. Association for Computational Linguistics (2023). https:\/\/doi.org\/10.18653\/V1\/2023.EACL-MAIN.148","DOI":"10.18653\/V1\/2023.EACL-MAIN.148"},{"key":"21_CR18","unstructured":"Nguyen, T., et al.: CulturaX: a cleaned, enormous, and multilingual dataset for large language models in 167 languages. In: Calzolari, N., Kan, M.Y., Hoste, V., Lenci, A., Sakti, S., Xue, N. (eds.) Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pp. 4226\u20134237 (2024)"},{"key":"21_CR19","unstructured":"van\u00a0den Oord, A., Li, Y., Vinyals, O.: Representation Learning with Contrastive Predictive Coding. arXiv:1807.03748 (2018)"},{"key":"21_CR20","unstructured":"OpenAI: New embedding models and API updates (2024). https:\/\/openai.com\/index\/new-embedding-models-and-api-updates\/, OpenAI Blog"},{"key":"21_CR21","unstructured":"Reimers, N., Choi, E., Kayid, A., Nandula, A., Govindassamy, M., Elkady, A.: Introducing Embed v3 (2024). https:\/\/cohere.com\/blog\/introducing-embed-v3, cohere Blog"},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using siamese BERT-networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3982\u20133992 (2019)","DOI":"10.18653\/v1\/D19-1410"},{"key":"21_CR23","unstructured":"Sturua, S., et al.: jina-embeddings-v3: Multilingual Embeddings With Task LoRA (2024)"},{"key":"21_CR24","doi-asserted-by":"crossref","unstructured":"Su, H., et al.: One embedder, any task: instruction-finetuned text embeddings. In: Findings of the Association for Computational Linguistics: ACL 2023, pp. 1102\u20131121 (2023)","DOI":"10.18653\/v1\/2023.findings-acl.71"},{"key":"21_CR25","volume":"568","author":"J Su","year":"2024","unstructured":"Su, J., Ahmed, M., Lu, Y., Pan, S., Bo, W., Liu, Y.: Roformer: enhanced transformer with rotary position embedding. Neurocomputing 568, 127063 (2024)","journal-title":"Neurocomputing"},{"key":"21_CR26","unstructured":"Wang, L., Yang, N., Huang, X., Yang, L., Majumder, R., Wei, F.: Multilingual E5 Text Embeddings: A Technical Report. arXiv:2402.05672 (2024)"},{"key":"21_CR27","unstructured":"Wei, J., et al.: Finetuned Language Models Are Zero-Shot Learners (2022). https:\/\/arxiv.org\/abs\/2109.01652"}],"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\/978-3-031-88720-8_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T12:02:14Z","timestamp":1743768134000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-88720-8_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031887192","9783031887208"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-88720-8_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"3 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors are employed by Jina AI and performed this work as part of their employment. Jina AI may benefit commercially from the research presented here. The authors have made every effort to ensure that the research has been conducted and reported objectively.","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":"Lucca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 April 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"47","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2025.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}