Large Language Models Encoder Versus Decoder
%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.)
![[논문 리뷰] Machine Translation with Large Language Models: Decoder Only vs ...](https://moonlight-paper-snapshot.s3.ap-northeast-2.amazonaws.com/arxiv/machine-translation-with-large-language-models-decoder-only-vs-encoder-decoder-2.png)
![Evolution of Neural Networks to Language Models [Updated]](https://cdn.labellerr.com/Enhancing%20Home%20Service%20with%20Object%20Recognition/Language%20Models/0_bM9oRET5AGEdpaRv.webp)
![[논문 리뷰] A Large Encoder-Decoder Family of Foundation Models For ...](https://moonlight-paper-snapshot.s3.ap-northeast-2.amazonaws.com/arxiv/a-large-encoder-decoder-family-of-foundation-models-for-chemical-language-2.png)
%2C%20there%20has%20been%20a%20shift%20towards%20linearization-based%20methods%2C%20which%20process%20structured%20data%20as%20sequential%20token%20streams%2C%20diverging%20from%20approaches%20that%20explicitly%20model%20structure%2C%20often%20as%20a%20graph.%20Crucially%2C%20there%20remains%20a%20gap%20in%20our%20understanding%20of%20how%20these%20linearization-based%20methods%20handle%20structured%20data%2C%20which%20is%20inherently%20non-linear.%20This%20work%20investigates%20the%20linear%20handling%20of%20structured%20data%20in%20encoder-decoder%20language%20models%2C%20specifically%20T5.%20Our%20findings%20reveal%20the%20model's%20ability%20to%20mimic%20human-designed%20processes%20such%20as%20schema%20linking%20and%20syntax%20prediction%2C%20indicating%20a%20deep%2C%20meaningful%20learning%20of%20structure%20beyond%20simple%20token%20sequencing.%20We%20also%20uncover%20insights%20into%20the%20model's%20internal%20mechanisms%2C%20including%20the%20ego-centric%20nature%20of%20structure%20node%20encodings%20and%20the%20potential%20for%20model%20compression%20due%20to%20modality%20fusion%20redundancy.%20Overall%2C%20this%20work%20sheds%20light%20on%20the%20inner%20workings%20of%20linearization-based%20methods%20and%20could%20potentially%20provide%20guidance%20for%20future%20research.)
![[2304.04052] Decoder-Only or Encoder-Decoder? Interpreting Language ...](https://ar5iv.labs.arxiv.org/html/2304.04052/assets/x1.png)
![[논문 리뷰] A Multi-Encoder Frozen-Decoder Approach for Fine-Tuning Large ...](https://moonlight-paper-snapshot.s3.ap-northeast-2.amazonaws.com/arxiv/a-multi-encoder-frozen-decoder-approach-for-fine-tuning-large-language-models-0.png)
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