回译中诗意意图的悖论：评估大语言模型在中文翻译中的质量
The Paradox of Poetic Intent in Back-Translation: Evaluating the Quality of Large Language Models in Chinese Translation
AI总结：
本研究构建多样化语料库，利用BT-Fried评估系统对比大语言模型与传统翻译工具在中英翻译中的表现，揭示了“诗意意图悖论”并提出改进的BLEU变体，推进了对AI翻译文化保真度的理解。
摘要：
大语言模型（LLMs）的快速发展重塑了机器翻译的格局，但在中英翻译中，保留诗意意图、文化遗产以及处理专业术语方面仍存在挑战。本研究构建了一个多样化的语料库，包含中文科学术语、历史翻译悖论和文学隐喻。利用基于回译和Friedman检验的评估系统（BT-Fried），我们评估了六个主要LLMs（例如GPT-4.5、DeepSeek V3）和三种传统翻译工具的BLEU、CHRF、TER和语义相似度指标。主要发现包括：（1）科学摘要通常受益于回译，而传统工具在语言上独特的文本中优于LLMs；（2）LLMs在文化和文学保留方面存在困难，体现了“诗意意图悖论”；（3）一些模型表现出“逐字回译”，反映了涌现的记忆行为；（4）提出了一种使用Jieba分词和n-gram加权的新的BLEU变体。该研究有助于中文NLP性能的实证评估，并推进了对AI介导翻译中文化保真度的理解。
英文摘要：
The rapid advancement of large language models (LLMs) has reshaped the landscape of machine translation, yet challenges persist in preserving poetic intent, cultural heritage, and handling specialized terminology in Chinese-English translation. This study constructs a diverse corpus encompassing Chinese scientific terminology, historical translation paradoxes, and literary metaphors. Utilizing a back-translation and Friedman test-based evaluation system (BT-Fried), we evaluate BLEU, CHRF, TER, and semantic similarity metrics across six major LLMs (e.g., GPT-4.5, DeepSeek V3) and three traditional translation tools. Key findings include: (1) Scientific abstracts often benefit from back-translation, while traditional tools outperform LLMs in linguistically distinct texts; (2) LLMs struggle with cultural and literary retention, exemplifying the "paradox of poetic intent"; (3) Some models exhibit "verbatim back-translation", reflecting emergent memory behavior; (4) A novel BLEU variant using Jieba segmentation and n-gram weighting is proposed. The study contributes to the empirical evaluation of Chinese NLP performance and advances understanding of cultural fidelity in AI-mediated translation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
