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  ---
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- license: mit
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  language:
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- - "en"
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- - "zh"
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  pipeline_tag: text-generation
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  inference: false
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  fine-tuning: true
@@ -10,17 +9,19 @@ tags:
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  - generative error correction
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  - large language model
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  - LLaMA
 
 
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  ---
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  This repo releases the trained LLaMA-adapter weights in paper "Large Language Models are Efficient Learners of Noise-Robust Speech Recognition."
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- If you consider this work would be related or useful for your research, please consider to cite the work in ICLR 2024. Thank you.
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  ```bib
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- @article{hu2024large,
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  title={Large Language Models are Efficient Learners of Noise-Robust Speech Recognition},
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- author={Hu, Yuchen and Chen, Chen and Yang, Chao-Han Huck and Li, Ruizhe and Zhang, Chao and Chen, Pin-Yu and Chng, EnSiong},
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- journal={Proc. ICLR},
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  year={2024}
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  }
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- ```
 
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  ---
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+ license: apache-2.0
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  language:
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+ - en
 
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  pipeline_tag: text-generation
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  inference: false
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  fine-tuning: true
 
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  - generative error correction
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  - large language model
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  - LLaMA
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+ metrics:
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+ - wer
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  ---
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  This repo releases the trained LLaMA-adapter weights in paper "Large Language Models are Efficient Learners of Noise-Robust Speech Recognition."
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+ If you consider this work would be related or useful for your research, please kindly consider to cite the work in ICLR 2024. Thank you.
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  ```bib
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+ @inproceedings{hu2024large,
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  title={Large Language Models are Efficient Learners of Noise-Robust Speech Recognition},
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+ author={Hu, Yuchen and Chen, Chen and Yang, Chao-Han Huck and Li, Ruizhe and Zhang, Chao and Chen, Pin-Yu and Chng, Eng Siong},
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+ booktitle={International Conference on Learning Representations},
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  year={2024}
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  }
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+ ```