Translation Artifact

Translation artifacts, unintended characteristics introduced during language translation, pose significant challenges across various machine learning applications, including cross-lingual question answering and text-to-image generation. Current research focuses on identifying and mitigating these artifacts, employing techniques like data augmentation, error correction models (e.g., using LLMs for code translation repair), and bias-removal algorithms (such as iterative null-space projection) to improve the reliability and fairness of multilingual systems. Addressing translation artifacts is crucial for advancing the development of robust and equitable AI systems capable of handling diverse languages and avoiding biases stemming from translation processes.