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

arXiv: https://arxiv.org/abs/2406.07232 · HTML: https://mlsanity.com/paper/2406.07232/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 14 of 14)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://mlsanity.com/paper/2305.19118/) (2023)
- [DualGAN: Unsupervised Dual Learning for Image-to-Image Translation](https://mlsanity.com/paper/1704.02510/) (2017)
- [Generating Sequences by Learning to Self-Correct](https://mlsanity.com/paper/2211.00053/) (2022)
- [CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task](https://mlsanity.com/paper/2209.06243/) (2022)
- [Dual Learning for Machine Translation](https://mlsanity.com/paper/1611.00179/) (2016)
- [The unreasonable effectiveness of few-shot learning for machine translation](https://mlsanity.com/paper/2302.01398/) (2023)
- [LLMs cannot find reasoning errors, but can correct them given the error location](https://mlsanity.com/paper/2311.08516/) (2023)
- [Dual Supervised Learning](https://mlsanity.com/paper/1707.00415/) (2017)

## Built on by (most cited first, 16 of 16)

- [Evaluating o1-Like LLMs: Unlocking Reasoning for Translation through Comprehensive Analysis](https://mlsanity.com/paper/2502.11544/) (2025)
- [Multilingual Large Language Models: A Systematic Survey](https://mlsanity.com/paper/2411.11072/) (2024)
- [Generator-Assistant Stepwise Rollback Framework for Large Language Model Agent](https://mlsanity.com/paper/2503.02519/) (2025)
- [Question-guided Knowledge Graph Re-scoring and Injection for Knowledge Graph Question Answering](https://mlsanity.com/paper/2410.01401/) (2024)
- [MIT-10M: A Large Scale Parallel Corpus of Multilingual Image Translation](https://mlsanity.com/paper/2412.07147/) (2024)
- [EvaLearn: Quantifying the Learning Capability and Efficiency of LLMs via Sequential Problem Solving](https://mlsanity.com/paper/2506.02672/) (2025)
- [DuPO: Enabling Reliable LLM Self-Verification via Dual Preference Optimization](https://mlsanity.com/paper/2508.14460/) (2025)
- [LLM-based Translation Inference with Iterative Bilingual Understanding](https://mlsanity.com/paper/2410.12543/) (2024)
- [A Review of Incorporating Psychological Theories in LLMs](https://mlsanity.com/paper/2505.00003/) (2025)
- [Two Intermediate Translations Are Better Than One: Fine-tuning LLMs for Document-level Translation Refinement](https://mlsanity.com/paper/2504.05614/) (2025)
- [Adaptive Inner Speech-Text Alignment for LLM-based Speech Translation](https://mlsanity.com/paper/2503.10211/) (2025)
- [Psychology-Driven Enhancement of Humour Translation](https://mlsanity.com/paper/2507.09259/) (2025)
- [CoPiT: Cognitive Pivot Translation for Digraphic Low-Resource Mongolian in the Traditional Script](https://mlsanity.com/paper/2607.05849/) (2026)
- [Translation with Thought: Difficulty-Adaptive Reasoning via Reinforcement Learning for Multi-Domain Machine Translation](https://mlsanity.com/paper/2607.29287/) (2026)
- [PAMT: Process-Aligned Reinforcement Learning for Multi-Domain Machine Translation](https://mlsanity.com/paper/2608.03077/) (2026)
- [Iron: Intent-Aligned and Retrospective Dual Learning Framework for Enhancing Generalist Virtual Agents](https://mlsanity.com/paper/2608.27866/) (2026)

## Abstract (arXiv)

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.