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        <title>LinChance-NTU for Unconstrained WMT2024 Literary Translation</title>
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            <title>Proceedings of the Ninth Conference on Machine Translation</title>
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    <abstract>The rapid growth of deep learning has spurred significant advancements across industries, par- ticularly in machine translation through large language models (LLMs). However, translat- ing literary still presents challenges, including cross-cultural nuances, complex language struc- tures, metaphorical expressions, and cultural differences. To address these issues, this study utilizes the Llama and Phi models using both LoRA and full-parameter techniques, along-side a prompt-based translation system. Full-parameter tuning of the Llama-3-Chinese-8B-Instruct model was unsuccessful due to mem-ory constraints. In terms of the WMT task, the fully fine-tuned Phi 3 model was selected for submission due to its more natural and flu-ent translations. Nonetheless, results showed that LoRA and the prompt-based system sig- nificantly improved the Llama3 model’s perfor- mance, surpassing other models in BLEU and ROUGE evaluations.</abstract>
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%0 Conference Proceedings
%T LinChance-NTU for Unconstrained WMT2024 Literary Translation
%A Li, Kechen
%A Tao, Yaotian
%A Huang, Hongyi
%A Ji, Tianbo
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Ninth Conference on Machine Translation
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F li-etal-2024-linchance
%X The rapid growth of deep learning has spurred significant advancements across industries, par- ticularly in machine translation through large language models (LLMs). However, translat- ing literary still presents challenges, including cross-cultural nuances, complex language struc- tures, metaphorical expressions, and cultural differences. To address these issues, this study utilizes the Llama and Phi models using both LoRA and full-parameter techniques, along-side a prompt-based translation system. Full-parameter tuning of the Llama-3-Chinese-8B-Instruct model was unsuccessful due to mem-ory constraints. In terms of the WMT task, the fully fine-tuned Phi 3 model was selected for submission due to its more natural and flu-ent translations. Nonetheless, results showed that LoRA and the prompt-based system sig- nificantly improved the Llama3 model’s perfor- mance, surpassing other models in BLEU and ROUGE evaluations.
%U https://aclanthology.org/2024.wmt-1.99
%P 987-992

Markdown (Informal)

[LinChance-NTU for Unconstrained WMT2024 Literary Translation](https://aclanthology.org/2024.wmt-1.99) (Li et al., WMT 2024)

• LinChance-NTU for Unconstrained WMT2024 Literary Translation (Li et al., WMT 2024)

ACL

• Kechen Li, Yaotian Tao, Hongyi Huang, and Tianbo Ji. 2024. LinChance-NTU for Unconstrained WMT2024 Literary Translation. In Proceedings of the Ninth Conference on Machine Translation, pages 987–992, Miami, Florida, USA. Association for Computational Linguistics.