When developing a new large language model (LLM), choosing the right training data is critical. “What you train your model on will determine completely different abilities,” Ian Magnusson, AI researcher at the University of Washington and the Allen Institute for AI (Ai2), told The New Stack.
An AI’s training data affects efficiency, bias and accuracy. “Poorly selected datasets can amplify biases, dilute task performance and require massive downstream corrections,” Sreekanth Gopi, founder at NeuroHeart, told The New Stack.
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