Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations

• URL: http://arxiv.org/abs/2212.09865v2
• Date: Sat, 3 Jun 2023 22:51:39 GMT
• Title: Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations
• Authors: Xinxi Lyu, Sewon Min, Iz Beltagy, Luke Zettlemoyer, Hannaneh Hajishirzi
• Abstract summary: We introduce Z-ICL, a new zero-shot method that closes the gap by constructing pseudo-demonstrations for a given test input. evaluation on nine classification datasets shows that Z-ICL outperforms previous zero-shot methods by a significant margin.
• Score: 97.41375480696972
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although large language models can be prompted for both zero- and few-shot learning, performance drops significantly when no demonstrations are available. In this paper, we introduce Z-ICL, a new zero-shot method that closes the gap by constructing pseudo-demonstrations for a given test input using a raw text corpus. Concretely, pseudo-demonstrations are constructed by (1) finding the nearest neighbors to the test input from the corpus and pairing them with random task labels, and (2) applying a set of techniques to reduce the amount of direct copying the model does from the resulting demonstrations. Evaluation on nine classification datasets shows that Z-ICL outperforms previous zero-shot methods by a significant margin, and is on par with in-context learning with labeled training data in the few-shot setting. Overall, Z-ICL provides a significantly higher estimate of the zero-shot performance levels of a model, and supports future efforts to develop better pseudo-demonstrations that further improve zero-shot results.

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