引導大型語言模型實現機器翻譯個性化

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

基于大型语言模型（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的生产。然而，在风格要求较为隐晦且可能难以通过提示传达的场景中，这些系统仍面临挑战。我们探索了在低资源环境下个性化LLM生成翻译的各种策略，重点关注具有挑战性的文学翻译领域。我们研究了提示策略和推理时干预措施，以引导模型生成朝向个性化风格，并提出了一个对比框架，利用从稀疏自编码器中提取的潜在概念来识别显著的个性化属性。我们的结果表明，引导在保持翻译质量的同时实现了强烈的个性化。我们进一步考察了引导对LLM表示的影响，发现对个性化有相关影响的模型层在多示例提示和我们的引导方法下受到相似的影响，暗示了相似的机制在起作用。

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.