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    <abstract>“In the field of Natural Language Processing (NLP), Large-scale Language Models (LLMs) havedemonstrated exceptional capabilities across a variety of tasks, including question answering,classification, and particularly, natural language understanding. The integration of neural ma-chine translation with LLMs presents significant potential, transforming the paradigms of cross-lingual communication and information exchange. This study investigates the foundational as-pects of LLMs’ translation abilities and identifies effective training methodologies to equip themwith multilingual capacities. We specifically explore the optimal timing for introducing trans-lation capabilities to LLMs via supervised tasks, considering the inherent bilingual nature ofmachine translation. Key questions explored include whether it is more beneficial to integratemultiple languages during the pre-training or supervised fine-tuning (SFT) stages, how varia-tions in language ratios influence LLMs’ translation abilities, and whether longer or shorter textsare more effective for training these models. This research conducts a thorough investigationby training multiple LLMs from scratch with parameter scales in the billions and enhances therobustness of our findings by upgrading the language capabilities of pre-trained open-sourcemodels with parameter scales reaching tens of billions. The aim is to provide a detailed analysisthat elucidates the complexities of augmenting machine translation capabilities within LLMs.”</abstract>
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%0 Conference Proceedings
%T E3: Optimizing Language Model Training for Translation via Enhancing Efficiency and Effectiveness
%A Linqing, Chen
%A Weilei, Wang
%A Dongyang, Hu
%Y Sun, Maosong
%Y Liang, Jiye
%Y Han, Xianpei
%Y Liu, Zhiyuan
%Y He, Yulan
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G eng
%F linqing-etal-2024-e3
%X “In the field of Natural Language Processing (NLP), Large-scale Language Models (LLMs) havedemonstrated exceptional capabilities across a variety of tasks, including question answering,classification, and particularly, natural language understanding. The integration of neural ma-chine translation with LLMs presents significant potential, transforming the paradigms of cross-lingual communication and information exchange. This study investigates the foundational as-pects of LLMs’ translation abilities and identifies effective training methodologies to equip themwith multilingual capacities. We specifically explore the optimal timing for introducing trans-lation capabilities to LLMs via supervised tasks, considering the inherent bilingual nature ofmachine translation. Key questions explored include whether it is more beneficial to integratemultiple languages during the pre-training or supervised fine-tuning (SFT) stages, how varia-tions in language ratios influence LLMs’ translation abilities, and whether longer or shorter textsare more effective for training these models. This research conducts a thorough investigationby training multiple LLMs from scratch with parameter scales in the billions and enhances therobustness of our findings by upgrading the language capabilities of pre-trained open-sourcemodels with parameter scales reaching tens of billions. The aim is to provide a detailed analysisthat elucidates the complexities of augmenting machine translation capabilities within LLMs.”
%U https://aclanthology.org/2024.ccl-1.79/
%P 1023-1034

Markdown (Informal)

[E3: Optimizing Language Model Training for Translation via Enhancing Efficiency and Effectiveness](https://aclanthology.org/2024.ccl-1.79/) (Linqing et al., CCL 2024)

• E3: Optimizing Language Model Training for Translation via Enhancing Efficiency and Effectiveness (Linqing et al., CCL 2024)

ACL

• Chen Linqing, Wang Weilei, and Hu Dongyang. 2024. E3: Optimizing Language Model Training for Translation via Enhancing Efficiency and Effectiveness. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference), pages 1023–1034, Taiyuan, China. Chinese Information Processing Society of China.