The Shuffle Test is the most common task to evaluate whether NLP models can measure coherence in text. Most recent work uses direct supervision on the task; we show that by simply finetuning a RoBERTa model, we can achieve a near perfect accuracy of 97.8%, a state-of-the-art. We argue that this outstanding performance is unlikely to lead to a good model of text coherence, and suggest that the Shuffle Test should be approached in a Zero-Shot setting: models should be evaluated without being trained on the task itself. We evaluate common models in this setting, such as Generative and Bi-directional Transformers, and find that larger architectures achieve high-performance out-of-the-box. Finally, we suggest the k-Block Shuffle Test, a modification of the original by increasing the size of blocks shuffled. Even though human reader performance remains high (around 95% accuracy), model performance drops from 94% to 78% as block size increases, creating a conceptually simple challenge to benchmark NLP models. Code available: https://github.com/tingofurro/shuffle_test/

本文使用RoBERTa模型对Shuffle Test任务进行了微调，并取得了97.8%的准确率，但提议使用Zero-Shot设置来评估模型；并引入k-Block Shuffle Test作为简单的测试NLP模型能力的方法。

Transformer模型是否能够衡量文本的连贯性？重新探讨洗牌测试