%2C%20known%20for%20their%20exceptional%20reasoning%20capabilities%2C%20generalizability%2C%20and%20fluency%20across%20diverse%20domains%2C%20present%20a%20promising%20avenue%20for%20enhancing%20speech-related%20tasks.%20In%20this%20paper%2C%20we%20focus%20on%20integrating%20decoder-only%20LLMs%20to%20the%20task%20of%20speech-to-text%20translation%20(S2TT).%20We%20propose%20a%20decoder-only%20architecture%20that%20enables%20the%20LLM%20to%20directly%20consume%20the%20encoded%20speech%20representation%20and%20generate%20the%20text%20translation.%20Additionally%2C%20we%20investigate%20the%20effects%20of%20different%20parameter-efficient%20fine-tuning%20techniques%20and%20task%20formulation.%20Our%20model%20achieves%20state-of-the-art%20performance%20on%20CoVoST%202%20and%20FLEURS%20among%20models%20trained%20without%20proprietary%20data.%20We%20also%20conduct%20analyses%20to%20validate%20the%20design%20choices%20of%20our%20proposed%20model%20and%20bring%20insights%20to%20the%20integration%20of%20LLMs%20to%20S2TT.)
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