LLM -based Translation Inference with Iterative Bilingual Understanding (2025)

Abstract: 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).

## introduction in the field of machine translation (mt), translations based on llms (llm-mt) have become a research focus @xcite @xcite @xcite . numerous studies have shown that the remarkable understanding and generation capabilities of llms significantly improve translation performance @xcite @xcite @xcite . one type of translation paradigm can be summarized as follows: the llm first generates a contextual understanding of the sentence to be translated, and then, based on this understanding, the crew is recruiting some vases as background characters the film crew is hiring a few eye candy as background characters. however, our study found that when the llm generates incorrect contextual understanding of the sentence to be translated, it negatively affects translation quality (as shown in figure 1(b) ). these generated understanding errors lead to the introduction of misleading information during the translation process, particularly when dealing with complex concepts such as commonsense and cultural domains. we refer to this issue as understanding distortion of llms. this study manually evaluated the chinese-english test set in the commonsense domain (randomly sampled 200 sentences). it found that understanding distortion makes up 40% of translation errors, highlighting the importance of this issue ( § 5.5). to address this issue, we propose a new method called iterative bilingual understanding translation (ibut). the ibut method consists of four parts: understanding generation, alignment judgment, iterative refinement, and understanding-based translation. for understanding generation, ibut leverages the crosslinguistic capabilities of llms to generate contextual understanding for both the source and target languages. for the alignment judgment part, ibut uses the generated bilingual contextual understanding and employs dual learning from the translation task @xcite @xcite as supervisory signals to produce explicit verbal feedback. for the iterative refinement part, the verbal feedback act as a semantic gradient @xcite , providing llm with a clear direction for refinement, thereby iteratively refining the bilingual contextual understanding. for the undersderstanding-based translation part, llm-mt finally translates using the source sentence and refined bilingual contextual understanding. empirical evaluations were conducted on closedsource and open-source llms including chatgpt, gpt-4, alpaca, and vicuna, covering multiple domains (e.g., news, commonsense, and cultural translation benchmarks). the results show an average improvement of +1.3, +4.2, and +2.3 comet scores compared to the baseline, confirming the effectiveness of the ibut strategy. additionally, analysis experiments show that ibut positively enhances the quality of bilingual contextual understanding with each iteration, thereby improving translation performance. ## iterative bilingual understanding translation the poor understanding of translated sentences generated by llms leads to a decline in translation quality. to address this issue, we propose a new method called iterative bilingual understanding translation (ibut). ibut utilizes llms to generate bilingual contextual understanding of the source input and utilizes the dual learning of translation tasks to establish verbal feedback for iteratively refining this understanding. finally, the iterative refinement reduces errors in bilingual contextual understanding, thereby enhancing translation performance. the ibut consists of four parts: 1) understanding generation; 2) alignment judgment; 3) iterative refinement; 4) understanding-based translation. we use m t to denote a translation model based on llm, and lowercase letters s and t to represent sentences in the source language (l s ) and target language (l t ), respectively. that is, @xmath0, • • • , s[n]) and @xmath1, • • • , t[m]), where each s[i] and t[i] is a token. ## conclusion this paper presents iterative bilingual understanding translation (ibut), a method for improving llm-based machine translation (llm-mt) by addressing understanding distortion issue. ibut generates bilingual contextual understanding, uses dual learning to create a supervisory signal, and iteratively refines the understanding to enhance translation performance. the method shows strong results across general news, commonsense, and cultural mt tasks, with human evaluations validating its effectiveness.