Unlocking Text Difficulty with Language Models: A Comprehensive Examination of Augmentation Techniques

In this article, we explore the correlation between the character count of text data and its difficulty level. By analyzing a dataset of text samples with varying lengths, we found that as the character count increases, accuracy decreases. This relationship holds true for both mean and median correctness levels.
To better understand this phenomenon, we employed several augmentation techniques to manipulate the text data, including token cutoff, span cutoff, concept and question mask, crop, summarize, reverse, permute, and segment permute. These techniques removed or modified various parts of the input sequence, allowing us to examine how different elements affect accuracy.
Our findings indicate that character count is a significant factor in determining difficulty level. As the number of characters increases, the difficulty level also rises, which makes it more challenging for students to accurately complete tasks. This relationship holds true even when controlling for other factors such as the type of task and the amount of information provided.
To illustrate this concept, think of a recipe with too many ingredients. Just like how too many ingredients can make a dish more complicated and difficult to prepare, an excessive number of characters in a text can also make it harder for readers to comprehend and retain the information.
In conclusion, our study provides evidence that character count is a critical factor in determining difficulty level in text data. By manipulating different parts of the input sequence and analyzing their impact on accuracy, we demonstrate how character count affects the complexity of a task and ultimately, its difficulty level. These findings have implications for educators and researchers alike, as they highlight the importance of carefully considering the length and content of text when designing tasks or studying student performance.