Best Practices and Lessons Learned on Synthetic Data
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913464034787328 |
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| author | Liu, Ruibo Wei, Jerry Liu, Fangyu Si, Chenglei Zhang, Yanzhe Rao, Jinmeng Zheng, Steven Peng, Daiyi Yang, Diyi Zhou, Denny Dai, Andrew M. |
| author_facet | Liu, Ruibo Wei, Jerry Liu, Fangyu Si, Chenglei Zhang, Yanzhe Rao, Jinmeng Zheng, Steven Peng, Daiyi Yang, Diyi Zhou, Denny Dai, Andrew M. |
| contents | The success of AI models relies on the availability of large, diverse, and high-quality datasets, which can be challenging to obtain due to data scarcity, privacy concerns, and high costs. Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns. This paper provides an overview of synthetic data research, discussing its applications, challenges, and future directions. We present empirical evidence from prior art to demonstrate its effectiveness and highlight the importance of ensuring its factuality, fidelity, and unbiasedness. We emphasize the need for responsible use of synthetic data to build more powerful, inclusive, and trustworthy language models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_07503 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Best Practices and Lessons Learned on Synthetic Data Liu, Ruibo Wei, Jerry Liu, Fangyu Si, Chenglei Zhang, Yanzhe Rao, Jinmeng Zheng, Steven Peng, Daiyi Yang, Diyi Zhou, Denny Dai, Andrew M. Computation and Language The success of AI models relies on the availability of large, diverse, and high-quality datasets, which can be challenging to obtain due to data scarcity, privacy concerns, and high costs. Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns. This paper provides an overview of synthetic data research, discussing its applications, challenges, and future directions. We present empirical evidence from prior art to demonstrate its effectiveness and highlight the importance of ensuring its factuality, fidelity, and unbiasedness. We emphasize the need for responsible use of synthetic data to build more powerful, inclusive, and trustworthy language models. |
| title | Best Practices and Lessons Learned on Synthetic Data |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2404.07503 |