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

Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, Sameer Singh
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:12697-12706, 2021.

Abstract

GPT-3 can perform numerous tasks when provided a natural language prompt that contains a few training examples. We show that this type of few-shot learning can be unstable: the choice of prompt format, training examples, and even the order of the examples can cause accuracy to vary from near chance to near state-of-the-art. We demonstrate that this instability arises from the bias of language models towards predicting certain answers, e.g., those that are 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 by asking for its prediction when given a training prompt and a content-free test input such as "N/A". We then fit calibration parameters that cause the prediction for this input to be uniform across answers. On a diverse set of tasks, this contextual calibration procedure substantially improves GPT-3 and GPT-2’s accuracy (up to 30.0% absolute) across different choices of the prompt, while also making learning considerably more stable.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-zhao21c, title = {Calibrate Before Use: Improving Few-shot Performance of Language Models}, author = {Zhao, Zihao and Wallace, Eric and Feng, Shi and Klein, Dan and Singh, Sameer}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {12697--12706}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/zhao21c/zhao21c.pdf}, url = {https://proceedings.mlr.press/v139/zhao21c.html}, abstract = {GPT-3 can perform numerous tasks when provided a natural language prompt that contains a few training examples. We show that this type of few-shot learning can be unstable: the choice of prompt format, training examples, and even the order of the examples can cause accuracy to vary from near chance to near state-of-the-art. We demonstrate that this instability arises from the bias of language models towards predicting certain answers, e.g., those that are 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 by asking for its prediction when given a training prompt and a content-free test input such as "N/A". We then fit calibration parameters that cause the prediction for this input to be uniform across answers. On a diverse set of tasks, this contextual calibration procedure substantially improves GPT-3 and GPT-2’s accuracy (up to 30.0% absolute) across different choices of the prompt, while also making learning considerably more stable.} }
Endnote
%0 Conference Paper %T Calibrate Before Use: Improving Few-shot Performance of Language Models %A Zihao Zhao %A Eric Wallace %A Shi Feng %A Dan Klein %A Sameer Singh %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-zhao21c %I PMLR %P 12697--12706 %U https://proceedings.mlr.press/v139/zhao21c.html %V 139 %X GPT-3 can perform numerous tasks when provided a natural language prompt that contains a few training examples. We show that this type of few-shot learning can be unstable: the choice of prompt format, training examples, and even the order of the examples can cause accuracy to vary from near chance to near state-of-the-art. We demonstrate that this instability arises from the bias of language models towards predicting certain answers, e.g., those that are 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 by asking for its prediction when given a training prompt and a content-free test input such as "N/A". We then fit calibration parameters that cause the prediction for this input to be uniform across answers. On a diverse set of tasks, this contextual calibration procedure substantially improves GPT-3 and GPT-2’s accuracy (up to 30.0% absolute) across different choices of the prompt, while also making learning considerably more stable.
APA
Zhao, Z., Wallace, E., Feng, S., Klein, D. & Singh, S.. (2021). Calibrate Before Use: Improving Few-shot Performance of Language Models. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:12697-12706 Available from https://proceedings.mlr.press/v139/zhao21c.html.

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