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20 lines
1.2 KiB
Plaintext
20 lines
1.2 KiB
Plaintext
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# Best Practices and Lessons Learned on Synthetic Data for Language Models
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import {Bleed} from 'nextra-theme-docs'
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<iframe width="100%"
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height="415px"
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src="https://www.youtube.com/embed/YnlArBZJHY8?si=ZH3hFzwixUopxU5Z" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
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allowFullScreen
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/>
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This [paper](https://arxiv.org/abs/2404.07503) provides an overview of best practices and lessons learned on synthetic data for language models ans was published by Google DeepMind and other collaborators.
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It focuses on synthetic data and covers applications, challenges, and future directions. This is an important paper given the significant advancements we are seeing from the use of synthetic data in the field of AI.
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We know for sure that the more high-quality data we give these models, the better the performance. Creating synthetic data is not hard but ensuring its quality is really the challenge.
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The paper also discusses important topics when working with synthetic data such as ensuring quality, factuality, fidelity, unbiasedness, trustworthiness, privacy, and more.
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There are a lot of great references mentioned in the related work section as well.
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