{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T08:53:05Z","timestamp":1763196785140,"version":"3.45.0"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819533480","type":"print"},{"value":"9789819533497","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T00:00:00Z","timestamp":1763251200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T00:00:00Z","timestamp":1763251200000},"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\/978-981-95-3349-7_14","type":"book-chapter","created":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T08:49:52Z","timestamp":1763196592000},"page":"175-187","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving LLM-Based Document-Level MT with\u00a0Multi-Knowledge Fusion"],"prefix":"10.1007","author":[{"given":"Bin","family":"Liu","sequence":"first","affiliation":[]},{"given":"Xinglin","family":"Lyu","sequence":"additional","affiliation":[]},{"given":"Junhui","family":"Li","sequence":"additional","affiliation":[]},{"given":"Daimeng","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Min","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Shimin","family":"Tao","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,16]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Adams, G., Fabbri, A.R., Ladhak, F., Lehman, E., Elhadad, N.: From sparse to dense: GPT-4 summarization with chain of density prompting. CoRR abs\/2309.04269 (2023)","DOI":"10.18653\/v1\/2023.newsum-1.7"},{"key":"14_CR2","unstructured":"Agrawal, R., Turchi, M., Negri, M.: Contextual handling in neural machine translation: look behind, ahead and on both sides. In: Proceedings of EAMT (2018)"},{"key":"14_CR3","unstructured":"Bao, G., Zhang, Y., Teng, Z., Chen, B., Luo, W.: G-transformer for document-level machine translation. In: Proceedings of ACL (2021)"},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Cui, M., Du, J., Zhu, S., Xiong, D.: Efficiently exploring large language models for document-level machine translation with in-context learning. In: Findings of ACL (2024)","DOI":"10.18653\/v1\/2024.findings-acl.646"},{"key":"14_CR5","unstructured":"Dong, Y., Jiang, X., Jin, Z., Li, G.: Self-collaboration code generation via chatgpt. CoRR abs\/2304.07590 (2023)"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Gao, T., Yao, X., Chen, D.: Simcse: simple contrastive learning of sentence embeddings. In: Proceedings of EMNLP (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"He, Z., et al.: Exploring human-like translation strategy with large language models. Trans. Assoc. Comput. Linguistics 12 (2024)","DOI":"10.1162\/tacl_a_00642"},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Junczys-Dowmunt, M.: Microsoft translator at WMT 2019: towards large-scale document-level neural machine translation. In: Proceedings of WMT (2019)","DOI":"10.18653\/v1\/W19-5321"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Kallini, J., Papadimitriou, I., Futrell, R., Mahowald, K., Potts, C.: Mission: impossible language models. In: Proceedings of ACL (2024)","DOI":"10.18653\/v1\/2024.acl-long.787"},{"key":"14_CR10","unstructured":"Kocmi, T., Federmann, C.: Large language models are state-of-the-art evaluators of translation quality. In: Proceedings of EAMT (2023)"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Li, X.L., et al.: Contrastive decoding: open-ended text generation as optimization. In: Proceedings of ACL (2023)","DOI":"10.18653\/v1\/2023.acl-long.687"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, J., Jiang, J., Tao, S., Yang, H., Zhang, M.: P-Transformer: towards better document-to-document neural machine translation. IEEE\/ACM Trans. Audio Speech Language Process. 31 (2023)","DOI":"10.1109\/TASLP.2023.3313445"},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, J., Jiang, J., Zhang, M.: Enhancing document-level translation of large language model via translation mixed-instructions. CoRR abs\/2401.08088 (2024)","DOI":"10.2139\/ssrn.4871785"},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: Multilingual denoising pre-training for neural machine translation. Trans. Assoc. Comput. Linguistics 8 (2020)","DOI":"10.1162\/tacl_a_00343"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Lyu, X., Li, J., Gong, Z., Zhang, M.: Encouraging lexical translation consistency for document-level neural machine translation. In: Proceedings of EMNLP (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.262"},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Lyu, X., et al.: Dempt: decoding-enhanced multi-phase prompt tuning for making LLMS be better context-aware translators. In: Proceedings of EMNLP (2024)","DOI":"10.18653\/v1\/2024.emnlp-main.1131"},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"Maruf, S., Martins, A.F.T., Haffari, G.: Selective attention for context-aware neural machine translation. In: Proceedings of NAACL-HLT (2019)","DOI":"10.18653\/v1\/N19-1313"},{"key":"14_CR18","unstructured":"Meta: Introducing meta llama 3: the most capable openly available LLM to date (2024). https:\/\/ai.meta.com\/blog\/meta-llama-3\/"},{"key":"14_CR19","unstructured":"MistralAI: mistral nemo (2024). https:\/\/mistral.ai\/news\/mistral-nemo\/"},{"key":"14_CR20","unstructured":"OpenAI: Gpt-4o mini: advancing cost-efficient intelligence (2024). https:\/\/openai.com\/research\/gpt-4"},{"key":"14_CR21","unstructured":"Pu, X., Gao, M., Wan, X.: Summarization is (almost) dead. CoRR abs\/2309.09558 (2023)"},{"key":"14_CR22","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners (2019)"},{"key":"14_CR23","unstructured":"Rei, R., et al.: Comet-22: unbabel-ist 2022 submission for the metrics shared task. In: Proceedings of WMT (2022)"},{"key":"14_CR24","doi-asserted-by":"crossref","unstructured":"Tiedemann, J., Scherrer, Y.: Neural machine translation with extended context. In: Proceedings of DiscoMT (2017)","DOI":"10.18653\/v1\/W17-4811"},{"key":"14_CR25","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Proceedings of NIPS (2017)"},{"key":"14_CR26","unstructured":"Wang, L., et al.: Document-level machine translation with large language models. In: Proceedings of EMNLP. Association for Computational Linguistics (2023)"},{"key":"14_CR27","unstructured":"Wang, Y., et al.: Delta: an online document-level translation agent based on multi-level memory. CoRR (2024)"},{"key":"14_CR28","unstructured":"Wu, M., Vu, T.T., Qu, L., Foster, G., Haffari, G.: Adapting large language models for document-level machine translation. CoRR abs\/2401.06468 (2024)"},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Wu, Y., Hu, G.: Exploring prompt engineering with GPT language models for document-level machine translation: insights and findings. In: Proceedings of WMT (2023)","DOI":"10.18653\/v1\/2023.wmt-1.15"},{"key":"14_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Improving the transformer translation model with document-level context. In: Proceedings of EMNLP (2018)","DOI":"10.18653\/v1\/D18-1049"},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, P., Chen, B., Ge, N., Fan, K.: Long-short term masking transformer: a simple but effective baseline for document-level neural machine translation. In: Proceedings of EMNLP (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.81"},{"key":"14_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, T., Ladhak, F., Durmus, E., Liang, P., McKeown, K.R., Hashimoto, T.B.: Benchmarking large language models for news summarization. Trans. Assoc. Comput. Linguistics (2024)","DOI":"10.1162\/tacl_a_00632"}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Chinese Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3349-7_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T08:49:56Z","timestamp":1763196596000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3349-7_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,16]]},"ISBN":["9789819533480","9789819533497"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3349-7_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,16]]},"assertion":[{"value":"16 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NLPCC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF International Conference on Natural Language Processing and Chinese Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Urumqi","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nlpcc2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tcci.ccf.org.cn\/conference\/2025\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}