{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T07:02:15Z","timestamp":1784530935067,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819237609","type":"print"},{"value":"9789819237616","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"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":[[2027]]},"DOI":"10.1007\/978-981-92-3761-6_10","type":"book-chapter","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T06:13:53Z","timestamp":1784528033000},"page":"141-152","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging Specialized Language Model to\u00a0Compose Large Language Model for\u00a0Domain Adaptation in\u00a0Solving Multiple-Choice Questions"],"prefix":"10.1007","author":[{"given":"Yicong","family":"Liang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fu Lee","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,21]]},"reference":[{"key":"10_CR1","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877\u20131901 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Ke, Z., Ming, Y., Nguyen, X.-P., Xiong, C., Joty, S.: Demystifying domain-adaptive post-training for financial llms. arXiv preprint arXiv:2501.04961 (2025)","DOI":"10.18653\/v1\/2025.emnlp-main.1579"},{"key":"10_CR3","unstructured":"Chen, J., et\u00a0al.: Huatuogpt-ii, one-stage training for medical adaption of llms. arXiv preprint arXiv:2311.09774 (2023)"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhu, Y., Mengyuan, L.: Enhancing legal expertise in large language models through composite model integration: the development and evaluation of law-neo. In: Proceedings of the Natural Legal Language Processing Workshop 2024, pp. 33\u201341 (2024)","DOI":"10.18653\/v1\/2024.nllp-1.3"},{"key":"10_CR5","unstructured":"Ling, C., et\u00a0al.: Domain specialization as the key to make large language models disruptive: a comprehensive survey. ACM Comput. Surv. (2023)"},{"key":"10_CR6","unstructured":"Gao, Y., et al.: Retrieval-augmented generation for large language models: a survey, vol. 2, no. 1. arXiv preprint arXiv:2312.10997 (2023)"},{"key":"10_CR7","unstructured":"Bansal, R., et al.: LLM augmented LLMs: expanding capabilities through composition. arXiv preprint arXiv:2401.02412 (2024)"},{"key":"10_CR8","first-page":"9459","volume":"33","author":"P Lewis","year":"2020","unstructured":"Lewis, P., et al.: Retrieval-augmented generation for knowledge-intensive nlp tasks. Adv. Neural. Inf. Process. Syst. 33, 9459\u20139474 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10_CR9","unstructured":"Houlsby, N., et al.: Parameter-efficient transfer learning for NLP. In: International Conference on Machine Learning, pp. 2790\u20132799. PMLR (2019)"},{"key":"10_CR10","unstructured":"Hu, E.J., et\u00a0al.: Lora: low-rank adaptation of large language models. In: ICLR, vol. 1, no. 2, p. 3 (2022)"},{"key":"10_CR11","unstructured":"Liang, P., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022)"},{"key":"10_CR12","unstructured":"Robinson, J., Michael Rytting, C., Wingate, D.: Leveraging large language models for multiple choice question answering. arXiv preprint arXiv:2210.12353 (2022)"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Nguyen, H.C., Dang, H.P., Nguyen, T.L., Hoang, V., Nguyen, V.A.: Accuracy of latest large language models in answering multiple choice questions in dentistry: a comparative study. PLoS One 20(1), e0317423 (2025)","DOI":"10.1371\/journal.pone.0317423"},{"key":"10_CR14","unstructured":"Qwen Team. Qwen2.5: A party of foundation models (2024). https:\/\/qwenlm.github.io\/blog\/qwen2.5\/"},{"key":"10_CR15","unstructured":"Grattafiori, A., et\u00a0al.: The llama 3 herd of models. arXiv preprint arXiv:2407.21783 (2024)"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Xie, Y., Aggarwal, K., Ahmad, A.: Efficient continual pre-training for building domain specific large language models. In: Findings of the Association for Computational Linguistics ACL 2024, pp. 10184\u201310201 (2024)","DOI":"10.18653\/v1\/2024.findings-acl.606"},{"key":"10_CR17","unstructured":"Chen, J., et al.: Huatuogpt-o1, towards medical complex reasoning with LLMs. arXiv preprint arXiv:2412.18925 (2024)"},{"key":"10_CR18","unstructured":"Smith, C.: 10k-history-data-v3-alpaca (2024). https:\/\/huggingface.co\/datasets\/ambrosfitz\/10k_history_data_v3_alpaca"},{"key":"10_CR19","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"10_CR20","unstructured":"Taori, R., et al.: Alpaca: a strong, replicable instruction-following model. Stanford Center for Research on Foundation Models, vol. 3, no. 6, p. 7 (2023). https:\/\/crfm.stanford.edu\/2023\/03\/13\/alpaca.html"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Tanabe, K., Hirano, M., Matoya, K., Imajo, K., Sakaji, H., Noda, I.: Enhancing financial domain adaptation of language models via model augmentation. In: 2024 IEEE International Conference on Big Data (BigData), pp. 6661\u20136669. IEEE (2024)","DOI":"10.1109\/BigData62323.2024.10825292"}],"container-title":["Lecture Notes in Computer Science","Blended Learning. Innovations for Future Education"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3761-6_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T06:13:57Z","timestamp":1784528037000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3761-6_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,21]]},"ISBN":["9789819237609","9789819237616"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3761-6_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,21]]},"assertion":[{"value":"21 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICBL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Blended Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","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":"28 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icbl2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/hksmic.org.hk\/icbl\/2026\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}