Benchmarking and Improving Long-Text Translation with Large Language Models

Longyue Wang, Zefeng Du, Wenxiang Jiao, Chenyang Lyu, Jianhui Pang, Leyang Cui, Kaiqiang Song, Derek Wong, Shuming Shi, Zhaopeng Tu


Abstract
Recent studies have illuminated the promising capabilities of large language models (LLMs) in handling long texts. However, their performance in machine translation (MT) of long documents remains underexplored. This paper aims to shed light on how LLMs navigate this complex task, offering a comprehensive evaluation of their capabilities and limitations in long-text MT. First, we collect and construct an instruction-based benchmark dataset, specifically designed for the finetuning and evaluation of LLMs, encompassing multilingual, multi-domain, and document-level parallel data. Second, we conduct a comprehensive comparison between MT and LLM models concerning document-level translation. Our analysis uncovers that LLMs exhibit shortcomings in long-text domains, and their performance diminishes as document size escalates. By exploiting various extrapolation strategies, we enhance the capacity of LLMs to translate longer texts. We release data, code, and models at https://github.com/longyuewangdcu/Document-MT-LLM.
Anthology ID:
2024.findings-acl.428
Volume:
Findings of the Association for Computational Linguistics ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand and virtual meeting
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7175–7187
Language:
URL:
https://aclanthology.org/2024.findings-acl.428
DOI:
Bibkey:
Cite (ACL):
Longyue Wang, Zefeng Du, Wenxiang Jiao, Chenyang Lyu, Jianhui Pang, Leyang Cui, Kaiqiang Song, Derek Wong, Shuming Shi, and Zhaopeng Tu. 2024. Benchmarking and Improving Long-Text Translation with Large Language Models. In Findings of the Association for Computational Linguistics ACL 2024, pages 7175–7187, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
Cite (Informal):
Benchmarking and Improving Long-Text Translation with Large Language Models (Wang et al., Findings 2024)
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https://preview.aclanthology.org/nschneid-patch-4/2024.findings-acl.428.pdf