While large language models (LLMs) demonstrate remarkable success in multilingual translation, their internal core translation mechanisms, even at the fundamental word level, remain insufficiently understood. To address this critical gap, this work introduces a systematic framework for interpreting the mechanism behind LLM translation from the perspective of computational components. This paper first proposes subspace-intervened path patching for precise, fine-grained causal analysis, enabling the detection of components crucial to translation tasks and subsequently characterizing their behavioral patterns in human-interpretable terms. Comprehensive experiments reveal that translation is predominantly driven by a sparse subset of components: specialized attention heads serve critical roles in extracting source language, translation indicators, and positional features, which are then integrated and processed by specific multi-layer perceptrons (MLPs) into intermediary English-centric latent representations before ultimately yielding the final translation. The significance of these findings is underscored by the empirical demonstration that targeted fine-tuning a minimal parameter subset ($<5\%$) enhances translation performance while preserving general capabilities. This result further indicates that these crucial components generalize effectively to sentence-level translation and are instrumental in elucidating more intricate translation tasks.

翻译：尽管大型语言模型（LLMs）在多语言翻译任务中展现出卓越性能，但其内部核心翻译机制——即使在基础的词汇层面——仍未被充分理解。为填补这一关键空白，本研究从计算组件的角度出发，提出了一种系统性框架以阐释LLM翻译背后的机理。本文首先提出子空间干预路径修补方法，用于实现精确、细粒度的因果分析，从而检测对翻译任务至关重要的组件，并以人类可解释的方式描述其行为模式。综合实验表明，翻译过程主要由稀疏的组件子集驱动：专用注意力头在提取源语言特征、翻译指示符及位置信息方面发挥关键作用，这些特征随后由特定的多层感知机（MLPs）整合处理为以英语为中心的中间潜在表示，最终生成目标语言译文。这些发现的重要性通过实证得以凸显：针对性地微调极小比例的参数子集（$<5\%$）可在保持模型通用能力的同时提升翻译性能。该结果进一步表明，这些关键组件能有效泛化至句子级翻译任务，并为阐释更复杂的翻译机制提供了重要依据。