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Y. Xia, F. Tian, L. Wu
2017
Earlier work this paper cites.
M. Shoeybi, M. Patwary, R. Puri
Original
2019
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O. Kuchaiev, J. Li, H. Nguyen
Original
2019
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2019
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Original
2020
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2020
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2020
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Original
2021
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X. L. Li and P. Liang, “Prefix-tuning: Optimizing continuous prompts for generation,”
Original
2021
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Original
2021
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S. Min, X. Lyu, A. Holtzman, M. Artetxe, M. Lewis, H. Hajishirzi, and L. Zettlemoyer, “Rethinking the role of demonstrations: What makes in-context learning work?” in
2022
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2022
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Original
2022
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2023
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2023
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2024

Chain-of-Thought Prompting for Speech Translation

Hu, Ke, Chen, Zhehuai, Yang, Chao-Han Huck et al.

Understand

Large language models (LLMs) have demonstrated remarkable advancements in language understanding and generation.

Building on the success of text-based LLMs, recent research has adapted these models to use speech embeddings for prompting, resulting in Speech-LLM models that exhibit strong performance in automatic speech recognition (ASR) and automatic speech translation (AST).
In this work, we propose a novel approach to leverage ASR transcripts as prompts for AST in a Speech-LLM built on an encoder-decoder text LLM.
The Speech-LLM model consists of a speech encoder and an encoder-decoder structure Megatron-T5.

Built on

Y. Xia, F. Tian, L. Wu
2017
Earlier work this paper cites.
M. Shoeybi, M. Patwary, R. Puri
Original
2019
Earlier work this paper cites.
O. Kuchaiev, J. Li, H. Nguyen
Original
2019
Earlier work this paper cites.
B. Zhang and R. Sennrich, “Root mean square layer normalization,”
2019
Earlier work this paper cites.
T. B. Brown, “Language models are few-shot learners,”
Original
2020
Earlier work this paper cites.
K. Hu, T. N. Sainath
2020
Earlier work this paper cites.
C. Raffel, N. Shazeer, A. Roberts
2020
Earlier work this paper cites.
B. Lester, R. Al-Rfou, and N. Constant, “The power of scale for parameter-efficient prompt tuning,”
Original
2021
Earlier work this paper cites.
X. L. Li and P. Liang, “Prefix-tuning: Optimizing continuous prompts for generation,”
Original
2021
Earlier work this paper cites.
E. J. Hu, Y. Shen, P. Wallis
Original
2021
Earlier work this paper cites.
S. Min, X. Lyu, A. Holtzman, M. Artetxe, M. Lewis, H. Hajishirzi, and L. Zettlemoyer, “Rethinking the role of demonstrations: What makes in-context learning work?” in
2022
Earlier work this paper cites.
J. Wei, X. Wang, D. Schuurmans
2022
Earlier work this paper cites.
D. Le, A. Shrivastava, P. Tomasello
Original
2022
Earlier work this paper cites.
A. Chowdhery, S. Narang, J. Devlin
2023
Earlier work this paper cites.
Gemini Team, “Gemini: a family of highly capable multimodal models,”
Original
2023
Earlier work this paper cites.

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C.-H. H. Yang, Y. Gu, Y.-C. Liu, S. Ghosh, I. Bulyko, and A. Stolcke, “Generative speech recognition error correction with large language models and task-activating prompting,” in

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