SQUREL at TSAR 2025 Shared Task CEFR -Controlled Text Simplification with Prompting and Reinforcement Fine-Tuning (2025)

Abstract: This paper summarises the submissions of our team to the TSAR 2025 Shared Task on Readability-Controlled Text Simplification, which aims to create text simplifications balancing reduced linguistic complexity, meaning preservation, and fluency while meeting predefined target readability levels. We tested two different methods for CEFR-controlled simplification a conservative lexical pipeline relying on prompting LLMs to simplify sentences, and a setup employing reinforcement fine-tuning.

## introduction readability-controlled text simplification (rcts) aims to generate simplifications within specified readability levels while preserving the original meaning @xcite . while instructiontuned llms have been shown to be useful in zeroshot rcts, balancing readability control and meaning preservation remains challenging @xcite . this paper presents our participation in the tsar 2025 shared task on readability-controlled text simplification @xcite . the shared task invited participants to simplify sentences at the b1 and a2 cefr levels. due to the scarcity of labelled data for supervised training, we explore approaches that do not require high-quality parallel data labelled with cefr levels. we propose two methods for producing readability-controlled simplifications: the rest of the paper is structured as follows. section 2 describes the two methods we employed to obtain the outputs submitted to the shared task, followed by the discussion of the results in section 3. we also describe limitations of our work and provide concluding observations. the prompts and examples from the outputs can be found in the appendix a. ## results and discussion the lexical simplification system (run 3) we submitted achieves the highest original meaning preservation scores across all systems submitted to the shared task. due to careful meaning preservation, the lexical simplification system ranks higher than the models fine-tuned with grpo despite lower accuracy in achieving target cefr levels. the suboptimal rmse of the reinforcement fine-tuning method might be due to our choice to optimize the reward functions for cefr adjacency accuracy, which considers outputs successful if their cefr level is within one level of the specified target, leading to lower accuracy. our lexical simplification pipeline, does not employ a word sense disambiguation module to prefilter candidates based on the sense of a word to be replaced. instead we employ a large language model (gpt-4o-mini) to infer which word fits in the given context. this enables the model to select morphologically and syntactically well-formed substitutes without relying on a separate wsd component. ## conclusions in this paper, we presented two approaches on readability-controlled text simplification for the tsar 2025 shared task. the lexical pipeline based on strict cefr-constrained substitutions with light post-editing generally preserves source meaning but quite often fails to replace the problematic words, and at times introduces critical errors, showing that control alone does not preclude serious failures. the models fine-tuned with grpo are better at producing simplifications corresponding to the specified cefr level but this is quite often at the expense of keeping the original meaning. these results underline the limitations of both approaches: the lexical pipeline achieves better meaning preservation while often failing at achieving the required readability level. at the same time, although the fine-tuned systems produce simpler texts, the output often deviates form the original meaning while still not matching the required readability level perfectly.