2026
Exposing the Cracks: Vulnerabilities of Retrieval-Augmented LLM-based Machine Translation
AAAI 2026technical
REtrieval-Augmented LLM-based Machine Translation (REAL-MT) shows promise for knowledge-intensive tasks like idiomatic translation, but its reliability under noisy retrieval, a common challenge in real-world deployment, remains poorly understood. To address this gap, we propose a noise synthesis fra