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--- |
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library_name: transformers |
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tags: [language-model, spoken-language, catastrophic-forgetting] |
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--- |
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# Model Description |
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This model is designed to address catastrophic forgetting in spoken language models during end-to-end training. It leverages innovative mitigation strategies to enhance model retention and performance over time. For more details, please refer to the paper by Hsiao et al. (2025) [here](https://arxiv.org/abs/2505.17496). |
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# Model Usage |
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To use this model, please check the [GitHub repository](https://github.com/chiyuanhsiao/ForgetSLM) for installation instructions, example code, and detailed usage guidelines. Ensure you have the required dependencies installed. |
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# Citation |
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If you use this model in your research or applications, please cite it as follows: |
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**APA:** |
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Hsiao, C.-Y., Lu, K.-H., Chang, K.-W., Yang, C.-K., Chen, W.-C., & Lee, H.-y. (2025). Analyzing Mitigation Strategies for Catastrophic Forgetting in End-to-End Training of Spoken Language Models. arXiv. https://arxiv.org/abs/2505.17496 |
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**BibTeX:** |
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``` |
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@misc{hsiao2025analyzingmitigationstrategiescatastrophic, |
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title={Analyzing Mitigation Strategies for Catastrophic Forgetting in End-to-End Training of Spoken Language Models}, |
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author={Chi-Yuan Hsiao and Ke-Han Lu and Kai-Wei Chang and Chih-Kai Yang and Wei-Chih Chen and Hung-yi Lee}, |
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year={2025}, |
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eprint={2505.17496}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2505.17496}, |
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} |
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``` |