Layer-aware Dual-directional Modulation for Low-resource Machine Translation

Although Large Language Models (LLMs) have achieved remarkable success in Machine Translation (MT), a significant performance gap persists between high-resource and lowresource languages due to imbalanced pretraining data. In this paper, we first investigate the internal mechanisms driving this performance disparity from a layer-wise perspective. We propose a metric termed Activation Disparity (∆R) to quantify the activation divergence between high-and low-resource MT. Based on this metric, we distinguish between Task-Adaptive Layers (TAL, ∆R > 0) that encode task-specific signals and Legacy-Inert Layers (LIL, ∆R < 0) dominated by pre-trained bias. Leveraging this finding, we propose the Layeraware Dual-directional Modulation (LaDM). Integrated with Low-Rank Adaptation (LoRA), LaDM employs a sparse strategy to bidirectionally modulate optimization dynamics. Specifically, it amplifies contributions from TAL to accelerate feature consolidation while inhibiting LIL to dampen misaligned legacy biases. Extensive experiments on Chinese-toseven low-resource language translation using Llama-3.1, Qwen2.5, and Gemma-2 demonstrate that LaDM significantly outperforms standard LoRA fine-tuning, achieving an average improvement of 1.73 spBLEU. Code is available at https://github.com/zzssqqq/ LaDM .