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Journal of Scientific Information Research

Keywords

large language model; knowledge enhancement; retrieval-augmented generation; text translation; intelligent information processing

Abstract

[Purpose/significance] This paper aims to improve the translation quality of large language models and effectively alleviate the translation illusion problem, thereby enhancing cross-linguistic information retrieval capabilities. [Method/process] A translation generation method based on a knowledge enhancement framework is proposed. This framework optimizes the translation process from multiple dimensions, such as style, focus, and cultural adaptability, by combining external knowledge provided by the translation context building module and the knowledge base building and retrieval module, and then utilizing the guidance of the text attention module. [Result/conclusion] Experimental results show that the proposed method effectively enhances model performance. Specifically, on the WikiLingua, TED, and CCMatrix datasets, the model's BLEU scores improved by 5.29%, 5.94%, and 8.58%, respectively. In addition to traditional translation evaluation metrics, this paper also introduces a six-dimensional evaluation system based on large models and an evaluation experiment to address the translation illusion problem. Experimental results show that after applying the proposed framework to the large model, all metrics surpass those of existing mainstream translation tools. This research provides a new solution for text translation, which has significant practical implications for quickly and accurately understanding information from large amounts of foreign language data, and is helpful for intelligence gathering, analysis, and decision-making.

First Page

24

Last Page

35

Submission Date

03-Sep-2025

Revision Date

07-Nov-2025

Acceptance Date

08-Dec-2025

Published Date

01-Apr-2026

Reference

[1] 李亚超, 熊德意, 张民.神经机器翻译综述[J].计算机学报, 2018, 41(12): 2734-2755.
[2] 马畅, 田永红, 郑晓莉, 等.基于知识蒸馏的神经机器翻译综述[J].计算机科学与探索, 2024, 18(7): 1725-1747.
[3] 陈可佳, 费子阳, 陈景强, 等.文本风格迁移研究综述[J].软件学报, 2022, 33(12): 4668-4687.
[4] DABRE R, CHU C, KUNCHUKUTTAN A.A Survey of Multilingual Neural Machine Translation[J].ACM Computing Surveys(CSUR), 2019, 53(5): 1-99.
[5] OPENAI, ACHIAM J, ADLER S, et al.GPT-4 Technical Report[J].arXiv e-prints, arXiv: 2303.08774.
[6] GLM T, ZENG A, XU B, et al.ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools[J].arXiv e-prints, arXiv: 2406.12793.
[7] SUTSKEVER I, VINYALS O, LE Q V.Sequence to Sequence Learning with Neural Networks[J].arXiv e-prints, arXiv: 1409.3215.
[8] BAHDANAU D, CHO K, BENGIO Y.Neural Machine Translation by Jointly Learning to Align and Translate[J].arXiv e-prints, arXiv: 1409.0473.
[9] 李响, 刘洋, 陈伟, 等.利用单语数据改进神经机器翻译压缩模型的翻译质量[J].中文信息学报, 2019, 33(7): 46-55.
[10] 冯洋, 邵晨泽.神经机器翻译前沿综述[J].中文信息学报, 2020, 34(7): 1-18.
[11] ZHANG J J, ZONG C Q.Neural machine translation: Challenges, progress and future[J].Science China Technological Sciences, 2020, 63(10): 2028-2050.
[12] 陶箭.国际商贸日语翻译中应遵循的原则[J].山西财经大学学报, 2012, 34(S4): 208-209.
[13] 藤井省三, 林敏洁.鲁迅文学日语翻译思考: 以竹内好的《呐喊》为中心[J].华东师范大学学报(哲学社会科学版), 2018, 50(1): 73-78, 178.
[14] 张贞港, 余传明.基于知识增强的文本语义匹配模型研究[J].情报学报, 2024, 43(4): 416-429.
[15] 张殿元, 余传明.基于知识增强与多模态融合的医疗视觉问答模型[J].数据分析与知识发现, 2024, 8(Z1): 226-239.
[16] ZHAO Y, XIANG L, ZHU J, et al.Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity[C]//Proceedings of the 28th International Conference on Computational Linguistics.Barcelona, Online, 2020: 4495-4505.
[17] ZHAO Y, ZHANG J, ZHOU Y, et al.Knowledge graphs enhanced neural machine translation[C]//Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence.Yokohama, 2021: 4039-4045.
[18] LIU P F, YUAN W Z, FU J L, et al.Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing[J].ACM Comput.Surv, 2023, 55(9): 1-35.
[19] LEWIS P, PEREZ E, PIKTUS A, et al.Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks[C]//Advances in Neural Information Processing Systems(Vol.33).Online, 2020: 9459-9474.
[20] 李纲, 余辉, 毛进.基于多层语义相似的技术供需文本匹配模型研究[J].数据分析与知识发现, 2021, 5(12): 25-36.
[21] 余传明, 薛浩东, 江一帆.基于深度交互的文本匹配模型研究[J].情报学报, 2021, 40(10): 1015-1026.
[22] SAADANY H, ORASAN C.BLEU, METEOR, BERTScore: Evaluation of Metrics Performance in Assessing Critical Translation Errors in Sentiment-oriented Text[C]//Proceedings of the Translation and Interpreting Technology Online Conference TRITON 2021.Online, 2021: 48-56.
[23] VASWANI A, SHAZEER N, PARMAR N, et al.Attention Is All You Need[C]//Advances in Neural Information Processing Systems(Vol.30).California, 2017: 5998-6008.
[24] MAO Z L, SHEN Y B, CHU C H, et al.Meta Ensemble for Japanese-Chinese Neural Machine Translation: Kyoto-U+ECNU Participation to WAT 2020[C]//Proceedings of the 7th Workshop on Asian Translation.Suzhou, 2020: 64-71.
[25] LU H Y, HUANG H Y, MA S M, et al.Revamping Multilingual Agreement Bidirectionally via Switched Back-translation for Multilingual Neural Machine Translation[C]//Findings of the Association for Computational Linguistics.Abu Dhabi, 2022, 2024: 10463-10475.
[26] JIAO W X, WANG W X, HUANG J T, et al.Is ChatGPT A Good Translator?[EB/OL].(2023-01-20)[2024-09-28].https://github.com/wxjiao/Is-ChatGPT-A-Good-Translator.
[27] HENDY A, ABDELREHIM M, SHARAF A, et al.How Good Are GPT Models at Machine Translation?A Comprehensive Evaluation[J].arXiv e-prints, arXiv: 2302.09210.
[28] ZHANG B, HADDOW B, BIRCH A.Prompting Large Language Model for Machine Translation: A Case Study[C]//Proceedings of the 40th International Conference on Machine Learning.Hawaii, 2023, 202: 41092-41110.
[29] BROWN T B, MANN B, RYDER N, et al.Language Models are Few-Shot Learners[J].Advances in Neural Information Processing Systems(Vol.33), 2020, arXiv e-prints, arXiv: 1905.05395.
[30] HU L, LIU Z, ZHAO Z, et al.A Survey of Knowledge Enhanced Pre-Trained Language Models[J].IEEE Transactions on Knowledge and Data Engineering, 2024, 36(4): 1413-1430.
[31] 余传明, 李浩男, 安璐.基于深度学习的领域知识对齐模型研究: 知识网络视角[J].情报学报, 2020, 39(5): 521-533.
[32] CAI D, WANG Y, LIU L, et al.Recent Advances in Retrieval-Augmented Text Generation[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.New York, 2022: 3417-3419.
[33] GU J, WANG Y, CHO K, et al.Search Engine Guided Neural Machine Translation[C]//Proceedings of the AAAI Conference on Artificial Intelligence.Louisiana, 2018: 5063-5070.
[34] MARVIN G, HELLEN N, JJINGO D, et al.Prompt Engineering in Large Language Models [J].Data Intelligence and Cognitive Informatics, 2024: 387-402.
[35] ZHU Y H, WANG Z X, GAO J Y, et al.Prompting Large Language Models for Zero-Shot Clinical Prediction with Structured Longitudinal Electronic Health Record Data[J].arXiv e-prints, arXiv: 2402.01713.
[36] ZHANG B, HADDOW B, BIRCH A.Prompting Large Language Model for Machine Translation: A Case Study[C]//Proceedings of the 40th International Conference on Machine Learning.Hawaii, 2023: 41092-41110.
[37] RUBIN O, HERZIG J, BERANT J.Learning To Retrieve Prompts for In-Context Learning[C]//Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.Washington, 2022: 2655-2671.
[38] DU Z X, QIAN Y J, LIU X, et al.GLM: General Language Model Pretraining with Autoregressive Blank Infilling[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics.Dublin, 2022: 320-335.
[39] NALLAPATI R, ZHOU B, SANTOS C N D, et al.Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond[C]//Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning.Berlin, 2016: 280-290.
[40] REIMERS N, GUREVYCH I.Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks[J].arXiv e-prints, arXiv: 1908.10084.
[41] XU Y H, LV T C, CUI L, et al.LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing.Online, 2021: 2579-2591.
[42] LUONG M T, PHAM H, MANNING C D.Effective Approaches to Attention-based Neural Machine Translation[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.Lisbon, 2015: 1412-1421.
[43] PAPINENI K, ROUKOS S, WARD T, et al.BLEU: A method for automatic evaluation of machine translation[C]//Proceedings of the 40th Annual Meeting on Association for Computational Linguistics.Philadelphia, 2002: 311-318.
[44] BANERJEE S, LAVIE A.METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments[C]//ACL 2005 Workshop on Intrinsic and Extrinsic Evaluation Measures for MT and/or Summarization.Ann Arbor, 2005: 65-72.
[45] ZHANG T, KISHORE V, WU F, et al.BERTScore: Evaluating Text Generation with BERT[J].arXiv e-prints, arXiv: 1904.09675.
[46] SINGH A, EHTESHAM A, KUMAR S, et al.Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG[J].arXiv e-prints, arXiv: 2501.09136.

Digital Object Identifier (DOI)

10.19809/j.cnki.kjqbyj.2026.02.003

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