DUAL - REFLECT : Enhancing Large Language Models for Reflective Translation through Dual Learning Feedback Mechanisms (2024)

Abstract: Recently, large language models (LLMs) enhanced by self-reflection have achieved promising performance on machine transla004 tion. 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.

## introduction large language models (llms) have recently demonstrated remarkable abilities across a variety of tasks @xcite @xcite . notably, in the field of machine translation, llms have improved translation quality by adopting human-like methods of self-reflection @xcite . the self-reflection process primarily relies on using llms to iteratively refine initial drafts through feedback loops, a method that has been widely researched and explored @xcite @xcite @xcite @xcite @xcite @xcite . the lack of effective feedback limits the self-reflective capacity of large language models (llms), thereby affecting their continuous 🤔 determine whether the following two sentences provided by user convey the same meaning and style, including subtleties. ....the term "⽩了" in this context implies a sense...... the word "disdainful" captures this connotation ## approach: dual-reflect our dual-reflect framework consists of five key stages, described in detail as follows: ## analysis we thoroughly analyze our approach, with results primarily reported on commonsensemt @xmath0. ## case study this section presents a case study on the dual-reflect method, assessing its effectiveness and constraints via examples (detailed in appendix b.5). positive instances (figures 4 , 5 and 6 ) illustrate substantial enhancements in translation accuracy and semantic coherence due to its reflective and iterative processes. conversely, negative examples (figures 7 and 8 ) highlight the dependency of dual-reflect's success on back translation quality, suggesting limitations in its capacity for improvement. this underscores the method's reliance on the integrity of each cycle component for optimal performance. this achievement has break the world record. ## conclusion we introduced dual-reflect, an llm-based machine translation method, that leverages dual learning to improve reflection and performance, excelling in resource-limited and common sense reasoning scenarios, with human evaluations confirming its effectiveness.