Is it Possible to Modify Text to a Target Readability Level? An Initial
  Investigation Using Zero-Shot Large Language Models
Asma Farajidizaji Vatsal Raina Mark Gales
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.