Text simplification is a common task where the text is adapted to make it
easier to understand. Similarly, text elaboration can make a passage more
sophisticated, offering a method to control the complexity of reading
comprehension tests. However, text simplification and elaboration tasks are
limited to only relatively alter the readability of texts. It is useful to
directly modify the readability of any text to an absolute target readability
level to cater to a diverse audience. Ideally, the readability of
readability-controlled generated text should be independent of the source text.
Therefore, we propose a novel readability-controlled text modification task.
The task requires the generation of 8 versions at various target readability
levels for each input text. We introduce novel readability-controlled text
modification metrics. The baselines for this task use ChatGPT and Llama-2, with
an extension approach introducing a two-step process (generating paraphrases by
passing through the language model twice). The zero-shot approaches are able to
push the readability of the paraphrases in the desired direction but the final
readability remains correlated with the original text's readability. We also
find greater drops in semantic and lexical similarity between the source and
target texts with greater shifts in the readability.

文本简化和增加复杂性的任务是为了提高阅读理解的测试的复杂度控制而有限的。我们提出了一种新颖的可控阅读度的文本修改任务，其中通过生成 8 个不同目标阅读度级别的版本来实现对输入文本的阅读度的绝对修改。此任务的基准是使用 ChatGPT 和 Llama-2，引入了一个两步过程的扩展方法（通过两次经过语言模型的生成重述）。零样本方法能够将改写的阅读度推向所需方向，但最终的阅读度与原始文本的阅读度仍然存在相关性。我们还发现，阅读度的变化程度与源文本和目标文本之间的语义和词汇相似性的降低程度存在较大的相关性。