{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T11:06:58Z","timestamp":1780916818681,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":17,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819214648","type":"print"},{"value":"9789819214655","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\/978-981-92-1465-5_7","type":"book-chapter","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T10:19:13Z","timestamp":1780913953000},"page":"81-93","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RouteLlama: Proactive Disentanglement for\u00a0Robust Multi-domain Text Mining"],"prefix":"10.1007","author":[{"given":"Jiarui","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingzhe","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,9]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","unstructured":"Bowman, S., Angeli, G., Potts, C., Manning, C.D.: A large annotated corpus for learning natural language inference. In: Proceedings of the 2015 Conference On Empirical Methods In Natural Language Processing, pp. 632\u2013642. (2015)","DOI":"10.18653\/v1\/D15-1075"},{"key":"7_CR2","unstructured":"Chen, Z., Badrinarayanan, V., Lee, C.Y., Rabinovich, A.: Gradnorm: gradient normalization for adaptive loss balancing in deep multitask networks. In: International Conference on Machine Learning, pp. 794\u2013803. PMLR (2018)"},{"key":"7_CR3","unstructured":"Eastwood, C., Williams, C.K.I.: A framework for the quantitative evaluation of disentangled representations. In: 6th International Conference on Learning Representations (2018)"},{"key":"7_CR4","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, pp. 6894\u20136910 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"7_CR5","unstructured":"Hu, E.J., et al.: Lora: low-rank adaptation of large language models. In: International Conference on Learning Representations (2022)"},{"issue":"3","key":"7_CR6","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1109\/TBDATA.2019.2921572","volume":"7","author":"J Johnson","year":"2019","unstructured":"Johnson, J., Douze, M., J\u00e9gou, H.: Billion-scale similarity search with GPUS. IEEE Trans. Big Data 7(3), 535\u2013547 (2019)","journal-title":"IEEE Trans. Big Data"},{"key":"7_CR7","unstructured":"Lewis, R.A., Matton, K., Picard, R., Guttag, J.: Improving domain generalization in contrastive learning using domain-aware temperature control. In: NeurIPS 2023 Workshop on Distribution Shifts: New Frontiers with Foundation Models (2023)"},{"key":"7_CR8","first-page":"18878","volume":"34","author":"B Liu","year":"2021","unstructured":"Liu, B., Liu, X., Jin, X., Stone, P., Liu, Q.: Conflict-averse gradient descent for multi-task learning. Adv. Neural. Inf. Process. Syst. 34, 18878\u201318890 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Malik, B., Kashyap, A.R., Kan, M.Y., Poria, S.: Udapter-efficient domain adaptation using adapters. In: Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pp. 2249\u20132263 (2023)","DOI":"10.18653\/v1\/2023.eacl-main.165"},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"Ni, J., Li, J., McAuley, J.: Justifying recommendations using distantly-labeled reviews and fine-grained aspects. 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. 188\u2013197 (2019)","DOI":"10.18653\/v1\/D19-1018"},{"key":"7_CR11","unstructured":"Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)"},{"key":"7_CR12","unstructured":"Tian, Y., Zhang, B., Tu, Z., Chu, D.: Adapters selector: cross-domains and multi-tasks lora modules integration usage method. In: Proceedings of the 31st International Conference on Computational Linguistics, pp. 593\u2013605 (2025)"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Williams, A., Nangia, N., Bowman, S.: A broad-coverage challenge corpus for sentence understanding through inference. In: Proceedings of the 2018 conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, volume 1 (long papers), pp. 1112\u20131122 (2018)","DOI":"10.18653\/v1\/N18-1101"},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"Wu, C., Jiang, G., Lian, D.: Mitigate negative transfer with similarity heuristic lifelong prompt tuning. In: Findings of the Association for Computational Linguistics ACL 2024, pp. 10944\u201310959 (2024)","DOI":"10.18653\/v1\/2024.findings-acl.650"},{"key":"7_CR15","unstructured":"Ye, H., Chen, S., Zhang, H., Luo, W., Li, Y., Zhang, X.: Synergy over discrepancy: a partition-based approach to multi-domain LLM fine-tuning. In: The Thirty-ninth Annual Conference on Neural Information Processing Systems (2025)"},{"key":"7_CR16","first-page":"5824","volume":"33","author":"T Yu","year":"2020","unstructured":"Yu, T., Kumar, S., Gupta, A., Levine, S., Hausman, K., Finn, C.: Gradient surgery for multi-task learning. Adv. Neural. Inf. Process. Syst. 33, 5824\u20135836 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Zhang, Z., et al.: Proactive gradient conflict mitigation in multi-task learning: a sparse training perspective. arXiv preprint arXiv:2411.18615 (2024)","DOI":"10.32388\/XLQW1L"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-1465-5_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T10:19:24Z","timestamp":1780913964000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-1465-5_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819214648","9789819214655"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-1465-5_7","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":"9 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"9 June 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 June 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pakdd2026.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}