From the 1 of 6 linked papers with an AI index.
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Do LLMs Need Architectural Changes for Simultaneous Speech Translation? A Prefix-to-Prefix Data Driven Approach
Junkun Chen, Jian Xue, Ming Tang +4
The paper proposes a data‑driven prefix‑to‑prefix fine‑tuning method for simultaneous speech translation that works with decoder‑only LLMs without changing their architecture, usin…
PHRASED: Phrase Dictionary Biasing for Speech Translation
Peidong Wang, Jian Xue, Rui Zhao +3
Phrases are essential to understand the core concepts in conversations. However, due to their rare occurrence in training data, correct translation of phrases is challenging in spe…
COSMIC: Data Efficient Instruction-tuning For Speech In-Context Learning
Jing Pan, Jian Wu, Yashesh Gaur +4
We present a cost-effective method to integrate speech into a large language model (LLM), resulting in a Contextual Speech Model with Instruction-following/in-context-learning Capa…
Soft Language Identification for Language-Agnostic Many-to-One End-to-End Speech Translation
Peidong Wang, Jian Xue, Jinyu Li +2
Language-agnostic many-to-one end-to-end speech translation models can convert audio signals from different source languages into text in a target language. These models do not nee…