New Contextual Calibration Method Boosts GPT-3 Accuracy Up to 30% | Synced
A research team from UC Berkeley, University of Maryland and UC Irvine identifies pitfalls that cause instability in the GPT-3 language model and proposes a contextual calibration procedure that im...
Source: Synced | AI Technology & Industry Review
A research team from UC Berkeley, University of Maryland and UC Irvine identifies pitfalls that cause instability in the GPT-3 language model and proposes a contextual calibration procedure that improves accuracy by up to 30 percent.