The Effects of Data Augmentation on Confidence Estimation for LLMs
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913890765373440 |
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| author | Wang, Rui Zhu, Renyu Lin, Minmin Wu, Runze Lv, Tangjie Fan, Changjie Wang, Haobo |
| author_facet | Wang, Rui Zhu, Renyu Lin, Minmin Wu, Runze Lv, Tangjie Fan, Changjie Wang, Haobo |
| contents | Confidence estimation is crucial for reflecting the reliability of large language models (LLMs), particularly in the widely used closed-source models. Utilizing data augmentation for confidence estimation is viable, but discussions focus on specific augmentation techniques, limiting its potential. We study the impact of different data augmentation methods on confidence estimation. Our findings indicate that data augmentation strategies can achieve better performance and mitigate the impact of overconfidence. We investigate the influential factors related to this and discover that, while preserving semantic information, greater data diversity enhances the effectiveness of augmentation. Furthermore, the impact of different augmentation strategies varies across different range of application. Considering parameter transferability and usability, the random combination of augmentations is a promising choice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11046 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Effects of Data Augmentation on Confidence Estimation for LLMs Wang, Rui Zhu, Renyu Lin, Minmin Wu, Runze Lv, Tangjie Fan, Changjie Wang, Haobo Machine Learning Confidence estimation is crucial for reflecting the reliability of large language models (LLMs), particularly in the widely used closed-source models. Utilizing data augmentation for confidence estimation is viable, but discussions focus on specific augmentation techniques, limiting its potential. We study the impact of different data augmentation methods on confidence estimation. Our findings indicate that data augmentation strategies can achieve better performance and mitigate the impact of overconfidence. We investigate the influential factors related to this and discover that, while preserving semantic information, greater data diversity enhances the effectiveness of augmentation. Furthermore, the impact of different augmentation strategies varies across different range of application. Considering parameter transferability and usability, the random combination of augmentations is a promising choice. |
| title | The Effects of Data Augmentation on Confidence Estimation for LLMs |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.11046 |