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    <abstract>We introduces ***LLaST***, a framework for building high-performance Large Language model based Speech-to-text Translation systems. We address the limitations of end-to-end speech translation (E2E ST) models by exploring model architecture design and optimization techniques tailored for LLMs. Our approach includes LLM-based speech translation architecture design, ASR-augmented training, multilingual data augmentation, and dual-LoRA optimization. Our approach demonstrates superior performance on the CoVoST-2 benchmark and showcases exceptional scaling capabilities powered by LLMs.We believe this effective method will serve as a strong baseline for speech translation and provide insights for futureimprovements of the LLM-based speech translation framework.</abstract>
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%0 Conference Proceedings
%T LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models
%A Chen, Xi
%A Zhang, Songyang
%A Bai, Qibing
%A Chen, Kai
%A Nakamura, Satoshi
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F chen-etal-2024-llast
%X We introduces ***LLaST***, a framework for building high-performance Large Language model based Speech-to-text Translation systems. We address the limitations of end-to-end speech translation (E2E ST) models by exploring model architecture design and optimization techniques tailored for LLMs. Our approach includes LLM-based speech translation architecture design, ASR-augmented training, multilingual data augmentation, and dual-LoRA optimization. Our approach demonstrates superior performance on the CoVoST-2 benchmark and showcases exceptional scaling capabilities powered by LLMs.We believe this effective method will serve as a strong baseline for speech translation and provide insights for futureimprovements of the LLM-based speech translation framework.
%U https://aclanthology.org/2024.findings-acl.416
%P 6976-6987

Markdown (Informal)

[LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models](https://aclanthology.org/2024.findings-acl.416) (Chen et al., Findings 2024)

• LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models (Chen et al., Findings 2024)

ACL

• Xi Chen, Songyang Zhang, Qibing Bai, Kai Chen, and Satoshi Nakamura. 2024. LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models. In Findings of the Association for Computational Linguistics ACL 2024, pages 6976–6987, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.