ITU NLP at TSAR 2025 Shared Task A Three-Stage Prompting Approach for CEFR -Oriented Text Simplification (2025)

Abstract: Automatic Text Simplification (TS) makes complex texts more accessible but often lacks control over target readability levels. We propose a lightweight, prompt-based approach to English TS that explicitly aligns outputs with CEFR proficiency standards. Our method employs a three-stage pipeline, guided by rule-informed prompts inspired by expert strategies. In the TSAR 2025 Shared Task, our system achieved competitive performance, with stronger results at B1 level and challenges at A2 level due to over-simplification. These findings highlight the promise of prompt-based CEFR-oriented simplification and the need for more flexible constraint design.

## introduction automatic text simplification (ts) is an important nlp task that aims to rewrite complex texts into simpler forms while preserving meaning. ts benefits many groups, including non-native speakers, children, and individuals with reading or cognitive difficulties. however, current systems often simplify texts without controlling the target readability level, limiting their usefulness in education and accessibility, where alignment with standardized frameworks such as the common european framework of reference for languages (cefr) @xcite is essential. we propose a cefr-oriented method for english text simplification that leverages large language models (llms) through prompt engineering rather than model training, making it lightweight, accessible, and practical. our method is inspired by the ts strategies introduced in @xcite . it applies a three-stage process: syntactic simplification, lexical simplification, and elaboration. this method addresses a key gap in current nlp research by combining accessibility with level-appropriate simplification. early text simplification research largely focused on sentence-level approaches. however, applying these methods iteratively across entire documents often fails to maintain discourse coherence and overall text integrity @xcite . this limitation has encouraged research to shift towards document-level simplification, which demands broader contextual understanding and specialized evaluation methods @xcite . llms have recently become prominent tools for document-level text simplification, addressing coherence and context issues. approaches such as progds @xcite decompose the simplification process into hierarchical stages (discourselevel, topic-level, and lexical-level), guiding llms to progressively simplify documents while preserving logical structure and readability. building on this line of work, our study also adopts a hierarchical paradigm, though the definition and scope of the stages differ from progds. in parallel, fine-tuning of llms has been shown to yield significant improvements in document-level text simplification tasks, effectively improving model accuracy and output relevance @xcite @xcite . unfortunately, none of these approaches are explicitly oriented toward cefr-based simplification. ## method our method of simplification consists of three stages that are carried out in a progressive order: syntactic simplification, lexical simplification, and elaboration. syntactic simplification involves restructuring complex grammatical constructions to align with the requirements of the target cefr level. lexical simplification focuses on replacing difficult vocabulary with simpler alternatives appropriate for the target proficiency level. elaboration involves adding explanatory words or clarifying information to make the text more comprehensible and suited to the intended cefr level. in @xcite , caseby-case rules were provided for all three types of simplifications at different proficiency levels, along with examples demonstrating how to execute these different simplification cases. although the examples were presented in turkish, the underlying simplification principles are not language-specific and reflect patterns observed across multiple languages @xcite . building on this foundation, @xcite incorporated these case-specific rules into turkish prompts and reported promising results. informed by both studies, we follow these rules when designing prompts and apply them sequentially in three stages foot_0 , in a manner inspired by @xcite . all stages utilize gpt-4o with the following parameters: ## results and discussion our three-stage progressive simplification pipeline yielded mixed results on the test data provided by the tsar 2025 shared task (alva-manchego et al., 2025), with overall meaningbert scores of 0.797 for both original-output (meaningbert original) and reference-output (meaningbert reference) comparisons, and a cefr compliance rmse of 0.632. analysis of the highest and lowest scoring texts revealed that our top-performing cases were predominantly b1-targeted, while most of the underperforming examples were a2-level simplifications. the five lowest meaningbert scores for both metrics came from our a2-level productions, whereas the top five scores for meaningbert original were all from b1-targeted simplifications. this performance gap stems largely from over-simplification induced by the strict a2-level linguistic constraints in the prompts, such as limited grammatical structures and restrictions on vocabulary exceeding a2 proficiency. as detailed in the methodology, our syntactic simplification step constrains gpt-4o to avoid grammatical structures outside the target cefr level, and the lexical simplification stage involves prompting gpt-4o to replace vocabulary that exceeds the target level. however, this approach can cause over-simplification in some cases. during the syntactic simplification process, the system output can sometimes distort the original meaning by avoiding grammatical structures that are not included in the target level's grammar list. an example of this can be seen in the a2-level simplification of text 73: ## conclusion this paper introduced a three-stage, cefroriented text simplification method using syntactic simplification, lexical simplification, and elaboration through prompt engineering with gpt-4o. the approach avoids costly model training while producing controlled simplifications aligned with language proficiency levels. the results indicate a more promising performance at b1, where the output preserved meaning and coherence, but also reveal limitations at a2, where rigid grammar and vocabulary constraints often led to over-simplification or unnecessary substitutions. these findings suggest that future work should have greater flexibility in constraint design to balance accessibility with fidelity.