Synthetic Data + LLMs = ??
Louis-Fran?ois Bouchard
Making AI accessible. ?? What's AI on YouTube. Co-founder at Towards AI. ex-PhD Student.
Good morning everyone! Nvidia just entered the LLM competition! In this iteration, we are talking about Nvidia's most recent publication, Nemotron-4-340B, which has the particularity of leveraging artificially generated data using its own model to train and refine its results.
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2?? Training LLMs with Synthetic Data...
Have you ever wondered why training large language models is such a massive challenge?
The secret is the enormous amount of high-quality data these models need. But getting that data is incredibly tough.
While many people have tried to solve this problem in various ways, one of the most promising approaches is using synthetic data. It’s less expensive than other methods, but it does have a major drawback: the lack of diversity.
Recently, Nvidia’s new LLMs from their Nemotron family of models have addressed this issue. They’ve shared a pipeline for generating synthetic data that’s used for training and refining Nemotron-4-340B. Let's dive in!
Watch the video (or article version):
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Louis-Fran?ois Bouchard
AI & ML Innovator | Transforming Data into Revenue | Expert in Building Scalable ML Solutions | Ex-Microsoft
3 个月It sounds like Nvidia's Nemotron-4 340B is leveraging some advanced techniques like LLMs, synthetic data, and iterative alignment to enhance their training process. These methods aim to improve the model's performance by using simulated data and refining its learning over multiple iterations.Historically, similar approaches have been used to push the boundaries of AI capabilities. For instance, in medical imaging, synthetic data has helped train AI models to detect diseases more accurately. Iterative alignment methods have also been crucial in fields like robotics, where fine-tuning models gradually improves their task performance.A profound question for experts in this field could be: How do you balance the trade-offs between using synthetic data for training and ensuring real-world applicability and reliability of AI models?