从话语到生动性：通过自适应局部偏好优化训练表达性字幕翻译LLM
From Utterance to Vividity: Training Expressive Subtitle Translation LLM via Adaptive Local Preference Optimization
AI总结：
本研究提出ALPO方法，通过构建多方向字幕语料库，优化翻译LLM的表达性和生动性，提升翻译质量的多维评估性能。
摘要：
大型语言模型（LLMs）的快速发展显著增强了机器翻译的通用能力。然而，随着应用场景变得更加复杂，LLMs在垂直领域翻译中的局限性逐渐显现。本研究聚焦于如何构建满足领域定制需求的翻译LLMs。我们以视觉媒体字幕翻译为主题，探索如何训练表达性和生动性的翻译LLMs。我们调查了字幕翻译和其他领域中直译与意译的情况，验证了LLMs作为奖励模型和评估者在翻译中的可靠性。此外，为了训练表达性翻译LLM，我们构建并发布了多方向字幕平行语料库数据集，并提出了自适应局部偏好优化（ALPO）方法以解决细粒度偏好对齐问题。实验结果表明，ALPO在翻译质量的多维评估中表现出色。
英文摘要：
The rapid development of Large Language Models (LLMs) has significantly enhanced the general capabilities of machine translation. However, as application scenarios become more complex, the limitations of LLMs in vertical domain translations are gradually becoming apparent. In this study, we focus on how to construct translation LLMs that meet the needs of domain customization. We take visual media subtitle translation as our topic and explore how to train expressive and vivid translation LLMs. We investigated the situations of subtitle translation and other domains of literal and liberal translation, verifying the reliability of LLM as reward model and evaluator for translation. Additionally, to train an expressive translation LLM, we constructed and released a multidirectional subtitle parallel corpus dataset and proposed the Adaptive Local Preference Optimization (ALPO) method to address fine-grained preference alignment. Experimental results demonstrate that ALPO achieves outstanding performance in multidimensional evaluation of translation quality.
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。
