LLM-based Translation Inference with Iterative Bilingual Understanding
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909443096051712 |
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| author | Chen, Andong Chen, Kehai Xiang, Yang Bai, Xuefeng Yang, Muyun Feng, Yang Zhao, Tiejun zhang, Min |
| author_facet | Chen, Andong Chen, Kehai Xiang, Yang Bai, Xuefeng Yang, Muyun Feng, Yang Zhao, Tiejun zhang, Min |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12543 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLM-based Translation Inference with Iterative Bilingual Understanding Chen, Andong Chen, Kehai Xiang, Yang Bai, Xuefeng Yang, Muyun Feng, Yang Zhao, Tiejun zhang, Min Computation and Language Artificial Intelligence 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). |
| title | LLM-based Translation Inference with Iterative Bilingual Understanding |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2410.12543 |