arXiv:2602.04613v2 Announce Type: replace Abstract: Mechanistic Interpretability (MI) seeks to explain how neural networks implement their capabilities, but the scale of Large Language Models (LLMs) has limited prior MI work in Machine Translation (MT) to word-level analyses. We study sentence-level MT from a mechanistic perspective by analyzing attention heads to understand how LLMs internally encode and distribute translation functions. We decompose MT into two subtasks: producing text in the target language (i.e. target language identification) and preserving the input sentence's meaning (i
The increasing scale and complexity of LLMs necessitate advanced interpretability techniques to understand their internal mechanisms, especially in critical applications like machine translation.
Understanding how LLMs perform translation at a mechanistic level can lead to more robust, reliable, and controllable AI systems, impacting critical applications and future AI development.
The ability to disentangle meaning from language within LLMs provides a new level of insight into their internal workings, moving beyond black-box approaches to enhance their design, debugging, and ethical deployment.
- · AI researchers
- · LLM developers
- · Machine translation users
- · Opaque AI systems
- · Monolingual content creators
Improved understanding of LLM translation capabilities will lead to more accurate and nuanced machine translation services.
Enhanced interpretability could accelerate the development of specialized and domain-specific LLMs with higher performance and trust.
Deeper mechanistic understanding of AI could inform the development of truly multilingual foundational models, reducing biases and improving cross-cultural communication.
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