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Status: Unchecked ×Keyword: prompt selection ×Clear all

2 claims from 1 paper

1. Computer Science › Topic Modeling

   Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

   Lu, Bartolo, Moore, Riedel and Stenetorp · Annual Meeting of the Association for Computational Linguistics (ACL) · 2022

   The paper shows that the order of few-shot examples can swing language model performance from near state-of-the-art to random guessing, and proposes a way to pick good orderings without extra labelled data.

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   1. UncheckedThe paper reports that sensitivity to the order of few-shot examples appears across model sizes and sample subsets, and good orderings do not carry over between models.“We analyse this phenomenon in detail, establishing that: it is present across model sizes (even for the largest current models), it is not related to a specific subset of samples, and that a given good permutation for one model is not transferable to another.”
   2. UncheckedThe authors' method for choosing the order of few-shot examples gave a 13% relative improvement for GPT-family models on eleven text classification tasks.“Our method yields a 13% relative improvement for GPT-family models across eleven different established text classification tasks.”