Large language models (LLMs) have demonstrated strong machine translation capabilities for English-centric language pairs but underperform in direct non-English (x2x) translation.This work addresses this limitation through a synthetic data generation framework that leverages models' established English-to-x (en2x) capabilities.By extending English parallel corpora into omnidirectional datasets and developing an English-referenced quality evaluation proxy, we enable effective collection of high-quality x2x training data.Combined with preference-based optimization, our method achieves significant improvement across 72 x2x directions for widely used LLMs, while generalizing to enhance en2x performance.The results demonstrate that strategic exploitation of English-centric strengths can bootstrap comprehensive multilingual translation capabilities in LLMs.We release codes, datasets, and model checkpoints at

In recent years, deep neural networks (DNNs) have achieved state-of-the-art performance on a wide range of tasks.However, limitations in interpretability have hindered their applications in the real world.This work proposes to interpret neural networks by linear decomposition and finds that the ReLU-activated Transformer can be considered as a linear model on a single input.We further leverage the linearity of the model and propose a linear decomposition of the model output to generate local explanations.Our evaluation of sentiment classification and machine translation shows that our method achieves competitive performance in efficiency and fidelity of explanation.In addition, we demonstrate the potential of our approach in applications with examples of error analysis on multiple tasks.