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Kate Park: Data Engines for Vision and Language
The Gradient: Perspectives on AI
Exploring Training Language Models with Trustworthy Data
Using trustworthy data is crucial for training language models effectively. Case studies show that even though ensuring trustworthy data is challenging, identifying reliable data labelers can lead to improved model performance. For instance, in the case of training GPT, initially, supervised fine-tuning data was provided by scale to customers like OpenAI where human labelers created question-answer pairs. However, the trend has shifted towards preference ratings and rankings for reinforcement learning, where labelers evaluate multiple model responses and rate the best one.
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