The paper "Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity" by Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp explores the impact of sample order on the performance of large, pre-trained language models (PLMs) such as GPT-3. The authors find that the order in which training samples are provided can significantly affect the model's performance, with some permutations leading to near state-of-the-art results while others perform randomly. This phenomenon is observed across different model sizes and tasks, and the order-sensitive prompts are not transferable between models. To address this issue, the authors propose a method that uses the generative nature of language models to construct an artificial development set, referred to as a "probing set." They then use entropy statistics from this set to identify performant prompts. The proposed method achieves an average 13% relative improvement across eleven text classification tasks, demonstrating its effectiveness and universality across different model sizes and tasks. The paper also includes a detailed analysis of the order sensitivity, the construction of the probing set, and the evaluation of the proposed method on various datasets.The paper "Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity" by Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp explores the impact of sample order on the performance of large, pre-trained language models (PLMs) such as GPT-3. The authors find that the order in which training samples are provided can significantly affect the model's performance, with some permutations leading to near state-of-the-art results while others perform randomly. This phenomenon is observed across different model sizes and tasks, and the order-sensitive prompts are not transferable between models. To address this issue, the authors propose a method that uses the generative nature of language models to construct an artificial development set, referred to as a "probing set." They then use entropy statistics from this set to identify performant prompts. The proposed method achieves an average 13% relative improvement across eleven text classification tasks, demonstrating its effectiveness and universality across different model sizes and tasks. The paper also includes a detailed analysis of the order sensitivity, the construction of the probing set, and the evaluation of the proposed method on various datasets.