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GPT-3 can perform numerous tasks when provided a natural language prompt thatcontains a few training examples. We show that this type of few-shot learningcan be unstable: the choice of prompt format, training examples, and even theorder of the training examples can cause accuracy to vary from near chance tonear state-of-the-art. We demonstrate that this instability arises from thebias of language models towards predicting certain answers, e.g., those thatare placed near the end of the prompt or are common in the pre-training data.To mitigate this, we first estimate the model's bias towards each answer byasking for its prediction when given the training prompt and a content-freetest input such as "N/A". We then fit calibration parameters that cause theprediction for this input to be uniform across answers. On a diverse set oftasks, this contextual calibration procedure substantially improves GPT-3 andGPT-2's average accuracy (up to 30.0% absolute) and reduces variance acrossdifferent choices of the prompt.

Calibrate Before Use: Improving Few-Shot Performance of Language Models