The race to build ever-larger language models is being driven by the assumption that more pre-training data equals better performance. It’s no surprise that AI companies have been scrambling to find enough quality data to train their AI models, often resorting to creating synthetic data to build and fine-tune the AI models. But what if this core assumption is flawed?
The post New Study Warns of Catastrophic Overtraining in Large Language Models appeared first on HPCwire.
Article From: "Ali Azhar" Read full article »
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