引导大型语言模型实现机器翻译个性化

Steering Large Language Models for Machine Translation Personalization

May 22, 2025

作者: Daniel Scalena, Gabriele Sarti, Arianna Bisazza, Elisabetta Fersini, Malvina Nissim

cs.AI

摘要

基于大规模语言模型（LLMs）的高质量机器翻译系统，已简化了反映特定风格约束的个性化翻译生产。然而，在风格要求较为隐晦、难以通过提示明确传达的场景中，这些系统仍面临挑战。我们探索了在资源匮乏环境下个性化LLM生成翻译的多种策略，重点关注文学翻译这一复杂领域。我们研究了提示策略及推理时干预措施，以引导模型生成趋向个性化风格，并提出了一种利用稀疏自编码器提取潜在概念的对比框架，用以识别显著的个性化特征。实验结果表明，引导策略在保持翻译质量的同时，实现了强有力的个性化。我们进一步考察了引导对LLM表征的影响，发现对个性化有显著影响的模型层，在多示例提示与我们的引导方法下受到相似影响，暗示了相似的作用机制在发挥作用。

English

High-quality machine translation systems based on large language models (LLMs) have simplified the production of personalized translations reflecting specific stylistic constraints. However, these systems still struggle in settings where stylistic requirements are less explicit and might be harder to convey via prompting. We explore various strategies for personalizing LLM-generated translations in low-resource settings, focusing on the challenging literary translation domain. We explore prompting strategies and inference-time interventions for steering model generations towards a personalized style, and propose a contrastive framework exploiting latent concepts extracted from sparse autoencoders to identify salient personalization properties. Our results show that steering achieves strong personalization while preserving translation quality. We further examine the impact of steering on LLM representations, finding model layers with a relevant impact for personalization are impacted similarly by multi-shot prompting and our steering method, suggesting similar mechanism at play.