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Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>Domain-specific machine translation (MT) significantly benefits from large language models (LLMs) due to their strong instruction-following abilities and in-context learning (ICL) capabilities. Appropriate demonstration samples and feedback are essential for helping LLMs refine their translation outputs in real-world applications. However, the scarcity of in-domain samples and professional feedback creates practical limitations. Furthermore, the current ICL paradigm does not offer the fine-grained domain features in addition to parallel translation pairs. To address these challenges, we propose a pipeline that collects in-domain translations from LLMs and generates synthetic, human-like feedback for revising these translations. The translations and their corresponding feedback are stored together to build a demonstration database, with each instance paired with the original in-domain translation and its revision. During online translation, similar in-domain translations can be retrieved as revision demonstrations. This process guides LLMs in iteratively refining their outputs by learning from demonstrations. We evaluate the proposed pipeline using open-source models like Llama3-8B-Instruct and Mistral-7B-Instruct-v0.3, on five domain-specific benchmarks for English-centric, Chinese-centric, and Portuguese-centric translation. The results demonstrate the effectiveness of the pipeline in tailoring in-domain translations and improving translation performance compared to direct translation instructions. Additionally, we discuss the experimental results from the following perspectives: (1) the effectiveness of different in-context retrieval methods; (2) the observed differences across selected domains and language; (3) the quantitative analysis of sentence-level and word-level statistics; and (4) the effect of ICL retrieval database size and decoding parameters.<\/jats:p>","DOI":"10.1145\/3787498","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T20:36:29Z","timestamp":1769805389000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Domain Adaptive Machine Translation with Synthetic Feedback for Large Language Models"],"prefix":"10.1145","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9218-0246","authenticated-orcid":false,"given":"Xinyi","family":"Yang","sequence":"first","affiliation":[{"name":"Computer and Information Science, University of Macau","place":["Macao, Macao"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3175-6885","authenticated-orcid":false,"given":"Runzhe","family":"Zhan","sequence":"additional","affiliation":[{"name":"Computer and Information Science, University of Macau","place":["Macao, Macao"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5044-3019","authenticated-orcid":false,"given":"Junchao","family":"Wu","sequence":"additional","affiliation":[{"name":"Computer and Information Science, University of Macau","place":["Macao, Macao"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5214-2268","authenticated-orcid":false,"given":"Yue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Westlake University","place":["Hangzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8524-2006","authenticated-orcid":false,"given":"Xuebo","family":"Liu","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology Shenzhen","place":["Shenzhen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6629-170X","authenticated-orcid":false,"given":"Lidia S.","family":"Chao","sequence":"additional","affiliation":[{"name":"Computer and Information Science, University of Macau","place":["Macao, Macao"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4484-7700","authenticated-orcid":false,"given":"Yujia","family":"Huo","sequence":"additional","affiliation":[{"name":"School of Data Science and Information Engineering, Guizhou Minzu University","place":["Guiyang, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5307-7322","authenticated-orcid":false,"given":"Derek F.","family":"Wong","sequence":"additional","affiliation":[{"name":"Computer and Information Science, University of Macau","place":["Macao, Macao"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,2,17]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-acl.564"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.692"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.nlpcovid19-2.5"},{"key":"e_1_3_3_5_2","first-page":"1877","volume-title":"Advances in Neural Information Processing Systems","author":"Brown Tom","year":"2020","unstructured":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. 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