DUAL-REFLECT: Enhancing Large Language Models for Reflective Translation through Dual Learning Feedback Mechanisms

Anonymous

Abstract: 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.

Paper Type: short

Research Area: Machine Translation

Contribution Types: NLP engineering experiment

Languages Studied: English

Preprint Status: We are considering releasing a non-anonymous preprint in the next two months (i.e., during the reviewing process).

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A2 Elaboration For Yes Or No: This research focuses on issues within the machine translation domain, with experiments revealing no risk outputs. The choice of models is based on those trained via RLHF, aiming for theoretical alignment with human feedback.

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