Enhancing Readability-Controlled Text Modification with Readability Assessment and Target Span Prediction (2025)

Abstract: Readability-controlled text modification aims to rewrite an input text so that it reaches a target level of difficulty. This task is closely related to automatic readability assessment (ARA) since, depending on the difficulty level of the input text, it may need to be simplified or complexified. Most previous research in LLM-based text modification has focused on zero-shot prompting, without further input from ARA or guidance on text spans that most likely require revision. This paper shows that ARA models for texts and sentences, as well as predictions of text spans that should be edited, can enhance performance in readability-controlled text modification.

## introduction readability-controlled text modification aims to rewrite the input text so that it reaches a target level of difficulty @xcite . the nature of the modification depends on whether the difficulty level of the input text is higher, lower, or the same as the target level. if higher, the system should perform text simplification, which has been extensively studied @xcite @xcite . if lower, the system needs to "increase the lexical and syntactic complexity of a text" @xcite , a task that has been studied under various terms including "textual embellishment" @xcite , "text elaboration" @xcite and "complexification" @xcite . in in the most recent study on readabilitycontrolled text modification @xcite , zero-shot prompting of large language input (intermediate level) argentina is unhappy that the us outdoor clothing retailer, patagonia, is claiming a domain name that has been known far longer as a region of spectacular beauty that also has its own parliament ... output for target level "elementary" argentina is unhappy that the us outdoor clothing retailer, patagonia wants a domain name that ... output for target level "intermediate" argentina is unhappy that the us outdoor clothing retailer, patagonia, is claiming a domain name that has been known far longer as a region of spectacular beauty that ... output for target level "advanced" argentina has lodged an expression of its unhappiness that the us outdoor clothing retailer, patagonia, is claiming a domain name that ... this paper investigates whether ara and text modification training data can improve the performance of readability-controlled text modification. specifically, we address the following research questions: ara data can text modification performance be improved with automatic assessment of the difficulty of a text and/or of individual sentences? text modification data can text modification performance be improved with automatic identification of text spans that require editing? the rest of the paper is organized as follows. after a review of previous research (section 2), we describe the proposed auxiliary models for guiding the text modification process (section 3), and the prompt that incorporates information from these auxiliary models (section 4). we then present our dataset (section 5) and report automatic (section 6) and human (section 7) evaluation results. ## previous work readability-controlled text modification may be viewed as a paraphrasing task @xcite with an additional constraint, namely, that the output must be at the target level of difficulty. to our knowledge, there have been only three published studies on text modification. @xcite assessed the ability of gpt-4 turbo to make a text easier or harder, but did not require the output to reach a specific difficulty level. @xcite focused on text modification at the sentence level. t5 models were fine-tuned on sentence pairs from text simplification datasets; for sentence complexification, t5 models were fine-tuned with the inputs and outputs reversed. most related to our work, @xcite generated versions of the input text at different target fres, using zero-shot prompting of chatgpt and llama-2. in terms of the readability of the modified texts, their best model achieved 24.2% accuracy in reaching the target fres. in terms of the content of the modified texts, however, no evaluation was reported because of the lack of gold outputs. other studies have focused exclusively on either text complexification or text simplification. for the former task, @xcite trained an lstm model on the inverse of wikilarge, a simplification corpus based on simple and standard english wikipedia. the model is then applied to complexify the texts in a story corpus. @xcite (2019) adopted a similar lstm encoder-decoder, and reported both bleu and human evaluation. for text simplification, early studies tended to take the relative approach, i.e., to make the input text easier but without specifying a target difficulty level or audience @xcite @xcite . recent work has increasingly recognized the need for text simplification to an absolute target level @xcite . @xcite used lexical and syntactic complexity features, while @xcite applied deep reinforcement learning using a reward calculated based on the difference between the difficulty of the output sentence and the target difficulty. @xcite used a non-autoregressive model to iteratively edit the source sentence. @xcite predicts low-level control tokens for text simplification. similar to this work, more recent studies have exploited llms. for example, the simplifymytext system rewrites the input text in plain language @xcite . in the ex-pertease system, llm-based agents collaborate in text simplification playing the roles of the expert, the teacher, and the student @xcite . ## approach readability-controlled text modification requires judgment on text difficulty, and on the kinds of content that are most suitable for revision. therefore, it may potentially benefit from auxiliary models that can assess the difficulty of sentences (section 3.1) and texts (section 3.2), and predict the text spans that require revision (section 3.3). for llmbased text modification, the information produced by these auxiliary models can be incorporated into the prompt (section 4). ## prompt implementation the system prompt ( in the input text, asterisks are placed on each word that is predicted to require editing, according to the target span prediction model (section 3.3). finally, each sentence in the input text is shown with its difficulty level, as estimated by the sent ara model (section 3.1). as demonstrations, two sample input texts are shown with their gold output text and gold predictions from the sent ara, text ara and target span prediction models foot_0 . ## data newsela @xcite is a graded parallel corpus derived from 1,911 news articles. for each article, simplified versions have been composed by professional editors for students between grade 2 and grade 12. to facilitate learning of the revision patterns across the spectrum of difficulty levels, for each of these 1,911 articles, we retrieved three versions that span the grades: one version between grades 2 and 5, which we will refer to as the "elementary" level (ele); one version between grades 6 and 8, the "intermediate" level (int); and one version between grades 9 and 12, the "advanced" level (adv). in our experiments, each of these 5,733 texts is to be revised to all three target levels {ele,int,adv}. since the level of the source text is not disclosed to the system, it does not know whether the text should be simplified (int->ele, adv->ele, adv->int), complexified (ele->int, ele->adv, int->adv), or left unchanged (ele->ele, int->int, adv->adv). ## conclusion human editors often need to tailor a text for readers at different proficiency levels. readabilitycontrolled text modification aims to rewrite an input text so that it reaches a target level of difficulty. depending on the difficulty of the input text, it may need to be simplified or complexified. this paper has presented the first study on llm-based readability-controlled text modification that leverages ara and prediction of target spans, i.e. the parts of the input text that require editing. we trained ara models that can predict the difficulty level of a sentence or a text, and taggers that predict whether each word should be revised. the information from these auxiliary models are then incorporated into the prompt for the llm. experimental results on the newsela corpus showed that both the ara models and the target span prediction model improved the quality of the modified text. in future work, we plan to evaluate this approach on other text genres, and investigate whether finetuning an llm on text modification data can lead to further performance gains.