{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T07:02:25Z","timestamp":1780729345931,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819214617","type":"print"},{"value":"9789819214624","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\/978-981-92-1462-4_16","type":"book-chapter","created":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T06:47:46Z","timestamp":1780728466000},"page":"197-209","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["$$\\text {R}^2\\text {R}$$: A Post-training Framework for\u00a0Multi-domain Decoder-Only Rerankers"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2915-9385","authenticated-orcid":false,"given":"Hanwei","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingchen","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenghan","family":"Tai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingrui","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jijun","family":"Chi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hailin","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tung Sum Thomas","family":"Kwok","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufei","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sicheng","family":"Lyu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muzhi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingze","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyue","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0521-2077","authenticated-orcid":false,"given":"Xinyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,7]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Brown, A., Roman, M., Devereux, B.: A systematic literature review of retrieval-augmented generation: Techniques, metrics, and challenges (2025). https:\/\/arxiv.org\/abs\/2508.06401","DOI":"10.3390\/bdcc9120320"},{"key":"16_CR2","doi-asserted-by":"publisher","unstructured":"Cao, H., et al.: Context-aware query classification. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR \u201909, New York, NY, USA, pp. 3\u201310. Association for Computing Machinery (2009). https:\/\/doi.org\/10.1145\/1571941.1571945","DOI":"10.1145\/1571941.1571945"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Chen, J., Xiao, S., Zhang, P., Luo, K., Lian, D., Liu, Z.: Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation (2023)","DOI":"10.18653\/v1\/2024.findings-acl.137"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Q., Wang, C., Wang, D., Zhang, T., Li, W., He, X.: Lifelong knowledge editing for vision language models with low-rank mixture-of-experts (2025). https:\/\/arxiv.org\/abs\/2411.15432","DOI":"10.1109\/CVPR52734.2025.00883"},{"key":"16_CR5","unstructured":"Hu, E.J., et al.: Lora: low-rank adaptation of large language models (2021). https:\/\/arxiv.org\/abs\/2106.09685"},{"key":"16_CR6","unstructured":"Kalajdzievski, D.: Scaling laws for forgetting when fine-tuning large language models (2024). https:\/\/arxiv.org\/abs\/2401.05605"},{"key":"16_CR7","unstructured":"Kong, R., et al.: Lora-switch: boosting the efficiency of dynamic LLM adapters via system-algorithm co-design (2024). https:\/\/arxiv.org\/abs\/2405.17741"},{"key":"16_CR8","unstructured":"Li, C., Liu, Z., Xiao, S., Shao, Y.: Making large language models a better foundation for dense retrieval (2023)"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Li, F., Zhang, X., Yuan, J., Zhu, X.: Classifying what-type questions by head noun tagging. In: Scott, D., Uszkoreit, H. (eds.) Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), Manchester, UK, pp. 481\u2013488. Coling 2008 Organizing Committee (2008). https:\/\/aclanthology.org\/C08-1061\/","DOI":"10.3115\/1599081.1599142"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Li, H., et al.: Lexrag: benchmarking retrieval-augmented generation in multi-turn legal consultation conversation (2025). https:\/\/arxiv.org\/abs\/2502.20640","DOI":"10.1145\/3726302.3730340"},{"key":"16_CR11","unstructured":"Li, Y., Gao, V., Zhang, C., Torkamani, M.: Ensembles of low-rank expert adapters (2025). https:\/\/arxiv.org\/abs\/2502.00089"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, Z., Zhang, K., Dan, R., Jiang, S., Zhang, Y.: Chatdoctor: a medical chat model fine-tuned on a large language model meta-AI (LLAMA) using medical domain knowledge (2023). https:\/\/arxiv.org\/abs\/2303.14070","DOI":"10.7759\/cureus.40895"},{"key":"16_CR13","unstructured":"Liu, Y., Chen, H., Huang, W., Ni, Y., Imani, M.: Lune: Efficient LLM unlearning via Lora fine-tuning with negative examples. arXiv preprint arXiv:2512.07375 (2025)"},{"key":"16_CR14","unstructured":"Liu, Y., Chen, H., Huang, W., Ni, Y., Imani, M.: Recover-to-forget: gradient reconstruction from Lora for efficient LLM unlearning. arXiv preprint arXiv:2512.07374 (2025)"},{"key":"16_CR15","unstructured":"Liu, Y., Chung, W.Y., Chen, H., Yeung, C., Imani, M.: Are hypervectors enough? single-call LLM reasoning over knowledge graphs. arXiv preprint arXiv:2512.09369 (2025)"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Tai, Z., Wu, H., Hu, Q., Chi, J., He, H.: VeritasFi: an adaptable, multi-tiered rag framework for multi-modal financial question answering. arXiv preprint arXiv:2510.10828 (2025)","DOI":"10.1145\/3774904.3792795"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: Finsage: a multi-aspect rag system for financial filings question answering (2025). https:\/\/arxiv.org\/abs\/2504.14493","DOI":"10.1145\/3746252.3761587"},{"key":"16_CR18","unstructured":"Wang, X., et al.: Resona: improving context copying in linear recurrence models with retrieval. In: Proceedings of the Conference on Language Modeling (COLM) (2025). https:\/\/openreview.net\/forum?id=4mxQmpnawk"},{"key":"16_CR19","unstructured":"Xiao, S., Liu, Z., Zhang, P., Muennighoff, N.: C-pack: packaged resources to advance general Chinese embedding (2023)"},{"key":"16_CR20","unstructured":"Xiong, Y., Xie, X.: Oplora: orthogonal projection Lora prevents catastrophic forgetting during parameter-efficient fine-tuning (2025). https:\/\/arxiv.org\/abs\/2510.13003"},{"key":"16_CR21","unstructured":"Zhang, J., Liu, X., Hu, Y., Niu, C., Wu, F., Chen, G.: Ragrouter: learning to route queries to multiple retrieval-augmented language models (2025). https:\/\/arxiv.org\/abs\/2505.23052"},{"key":"16_CR22","unstructured":"Zhang, Y., et al.: Qwen3 embedding: advancing text embedding and reranking through foundation models. arXiv preprint arXiv:2506.05176 (2025)"},{"key":"16_CR23","unstructured":"Zhao, Y., et al.: Swift: a scalable lightweight infrastructure for fine-tuning (2024). https:\/\/arxiv.org\/abs\/2408.05517"},{"key":"16_CR24","unstructured":"Zhuang, Y., et al.: LD-mole: learnable dynamic routing for mixture of Lora experts (2025). https:\/\/arxiv.org\/abs\/2509.25684"}],"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\/978-981-92-1462-4_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T06:47:51Z","timestamp":1780728471000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-1462-4_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819214617","9789819214624"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-1462-4_16","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":"7 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"}}]}}