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%252C+known+for+their+exceptional+reasoning+capabilities%252C+generalizability%252C+and+fluency+across+diverse+domains%252C+present+a+promising+avenue+for+enhancing+speech-related+tasks.+In+this+paper%252C+we+focus+on+integrating+decoder-only+LLMs+to+the+task+of+speech-to-text+translation+(S2TT).+We+propose+a+decoder-only+architecture+that+enables+the+LLM+to+directly+consume+the+encoded+speech+representation+and+generate+the+text+translation.+Additionally%252C+we+investigate+the+effects+of+different+parameter-efficient+fine-tuning+techniques+and+task+formulation.+Our+model+achieves+state-of-the-art+performance+on+CoVoST+2+and+FLEURS+among+models+trained+without+proprietary+data.+We+also+conduct+analyses+to+validate+the+design+choices+of+our+proposed+model+and+bring+insights+to+the+integration+of+LLMs+to+S2TT.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.9e040bc9afec62bb78d16b29a3494aec%3frik%3dS%252fnla5oLS1XfMw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Decoded+Using+Internal+Representation+of+Language&FORM=IRPRST&ck=C0D02BB6C8BEFB78DE506D3AFA6F916A&selectedIndex=22&itb=0)
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