# LLM-based Translation Inference with Iterative Bilingual Understanding

arXiv: https://arxiv.org/abs/2410.12543 · HTML: https://mlsanity.com/paper/2410.12543/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 47 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Builds on (most cited first, 26 of 26)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [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)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [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)
- [How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation](https://mlsanity.com/paper/2302.09210/) (2023)
- [Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2010.00710/) (2020)
- [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)
- [Hallucinations in Large Multilingual Translation Models](https://mlsanity.com/paper/2303.16104/) (2023)
- [LLMs cannot find reasoning errors, but can correct them given the error location](https://mlsanity.com/paper/2311.08516/) (2023)
- [On the Effect of Pretraining Corpora on In-context Learning by a Large-scale Language Model](https://mlsanity.com/paper/2204.13509/) (2022)
- [Large language models effectively leverage document-level context for literary translation, but critical errors persist](https://mlsanity.com/paper/2304.03245/) (2023)
- [(Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Texts](https://mlsanity.com/paper/2405.11804/) (2024)
- [Empowering Cross-lingual Abilities of Instruction-tuned Large Language Models by Translation-following demonstrations](https://mlsanity.com/paper/2308.14186/) (2023)
- [Incorporating Discrete Translation Lexicons into Neural Machine Translation](https://mlsanity.com/paper/1606.02006/) (2016)
- [Domain Adaptation of Neural Machine Translation by Lexicon Induction](https://mlsanity.com/paper/1906.00376/) (2019)
- [How to Design Translation Prompts for ChatGPT: An Empirical Study](https://mlsanity.com/paper/2304.02182/) (2023)
- [Using natural language prompts for machine translation](https://mlsanity.com/paper/2202.11822/) (2022)
- [DUAL-REFLECT: Enhancing Large Language Models for Reflective Translation through Dual Learning Feedback Mechanisms](https://mlsanity.com/paper/2406.07232/) (2024)
- [DEEP: DEnoising Entity Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2111.07393/) (2021)
- [Improving Machine Translation with Large Language Models: A Preliminary Study with Cooperative Decoding](https://mlsanity.com/paper/2311.02851/) (2023)
- [Towards Effective Disambiguation for Machine Translation with Large Language Models](https://mlsanity.com/paper/2309.11668/) (2023)
- [Translating Phrases in Neural Machine Translation](https://mlsanity.com/paper/1708.01980/) (2017)

## Built on by (most cited first, 6 of 6)

- [Evaluating o1-Like LLMs: Unlocking Reasoning for Translation through Comprehensive Analysis](https://mlsanity.com/paper/2502.11544/) (2025)
- [Generator-Assistant Stepwise Rollback Framework for Large Language Model Agent](https://mlsanity.com/paper/2503.02519/) (2025)
- [Make Imagination Clearer! Stable Diffusion-based Visual Imagination for Multimodal Machine Translation](https://mlsanity.com/paper/2412.12627/) (2024)
- [Two Intermediate Translations Are Better Than One: Fine-tuning LLMs for Document-level Translation Refinement](https://mlsanity.com/paper/2504.05614/) (2025)
- [LinguaLIFT: An Effective Two-stage Instruction Tuning Framework for Low-Resource Language Reasoning](https://mlsanity.com/paper/2412.12499/) (2024)
- [TransLaw: A Large-Scale Dataset and Multi-Agent Benchmark Simulating Professional Translation of Hong Kong Case Law](https://mlsanity.com/paper/2507.00875/) (2025)

## Abstract (arXiv)

The remarkable understanding and generation capabilities of large language models (LLMs) have greatly improved translation performance. However, incorrect understanding of the sentence to be translated can degrade translation quality. To address this issue, we proposed a novel Iterative Bilingual Understanding Translation (IBUT) method based on the cross-lingual capabilities of LLMs and the dual characteristics of translation tasks. The cross-lingual capability of LLMs enables the generation of contextual understanding for both the source and target languages separately. Furthermore, the dual characteristics allow IBUT to generate effective cross-lingual feedback, iteratively refining contextual understanding, thereby reducing errors and improving translation performance. Experimental results showed that the proposed IBUT outperforms several strong comparison methods, especially being generalized to multiple domains (e.g., news, commonsense, and cultural translation benchmarks).