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While we’ve seen amazing improvements in model performance over the last several years, we must be aware of the remaining downsides of these models. We believe that by jointly improving these models as well as evolving our approaches to evaluating them is essential going forward.

We introduce an automated method for evaluating the quality of AI-generated text by repurposing prior human evaluation data. The method is called Near-Negative Distinction.