  Recently, large language models (LLMs) enhanced by self-reflection have
achieved promising performance on machine translation. The key idea is guiding
LLMs to generate translation with human-like feedback. However, existing
self-reflection methods lack effective feedback information, limiting the
translation performance. To address this, we introduce a DUAL-REFLECT
framework, leveraging the dual learning of translation tasks to provide
effective feedback, thereby enhancing the models' self-reflective abilities and
improving translation performance. The application of this method across
various translation tasks has proven its effectiveness in improving translation
accuracy and eliminating ambiguities, especially in translation tasks with
low-resource language pairs.
