mlsanityData to 14 Oct 2026

LLM-based Translation Inference with Iterative Bilingual Understanding

arXiv 2410.12543 ↗First posted 16 Oct 2024cs.CL cs.AIcited by 6

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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).

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

26 of 47 references.

References · 26

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
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14 more
  1. 2023LLMs cannot find reasoning errors, but can correct them given the error locationcited by 85
  2. 2022On the Effect of Pretraining Corpora on In-context Learning by a Large-scale Language Modelcited by 81
  3. 2023Large language models effectively leverage document-level context for literary translation, but critical errors persistcited by 80
  4. 2024(Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Textscited by 49
  5. 2023Empowering Cross-lingual Abilities of Instruction-tuned Large Language Models by Translation-following demonstrationscited by 35
  6. 2016Incorporating Discrete Translation Lexicons into Neural Machine Translationcited by 33
  7. 2019Domain Adaptation of Neural Machine Translation by Lexicon Inductioncited by 33
  8. 2023How to Design Translation Prompts for ChatGPT: An Empirical Studycited by 31
  9. 2022Using natural language prompts for machine translationcited by 27
  10. 2024DUAL-REFLECT: Enhancing Large Language Models for Reflective Translation through Dual Learning Feedback Mechanismscited by 16
  11. 2021DEEP: DEnoising Entity Pre-training for Neural Machine Translationcited by 15
  12. 2023Improving Machine Translation with Large Language Models: A Preliminary Study with Cooperative Decodingcited by 10
  13. 2023Towards Effective Disambiguation for Machine Translation with Large Language Modelscited by 8
  14. 2017Translating Phrases in Neural Machine Translationcited by 4

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Cited by year

Citing papers per year: 2024: 2; 2025: 4.

6 papers.

  1. 2025Evaluating o1-Like LLMs: Unlocking Reasoning for Translation through Comprehensive Analysiscited by 26
  2. 2025Generator-Assistant Stepwise Rollback Framework for Large Language Model Agentcited by 14
  3. 2024Make Imagination Clearer! Stable Diffusion-based Visual Imagination for Multimodal Machine Translationcited by 5
  4. 2025Two Intermediate Translations Are Better Than One: Fine-tuning LLMs for Document-level Translation Refinementcited by 5
  5. 2024LinguaLIFT: An Effective Two-stage Instruction Tuning Framework for Low-Resource Language Reasoningcited by 4
  6. 2025TransLaw: A Large-Scale Dataset and Multi-Agent Benchmark Simulating Professional Translation of Hong Kong Case Lawcited by 2