OUNLP at TSAR 2025 Shared Task Multi-Round Text Simplifier via Code Generation (2025)

Abstract: This paper describes the system submission of our team OUNLP to the TSAR-2025 shared task on readability-controlled text simplification. Based on the analysis of prompt-based text simplification methods, we discovered that simplification performance is highly related to the gap between the source CEFR level and the target CEFR level. Inspired by this finding, we propose two multi-round simplification methods generated via GPT-4o rule-based simplification (MRS-Rule) and jointly rule-based LLM simplification (MRS-Joint). Our submitted systems ranked 7th out of 20 teams. Later improvements with MRS-Joint show that taking the LLM simplified candidates as the starting point could further boost multi-round simplification performance.

## introduction complex text makes it difficult for language learners and people with limited literacy to read. text simplification improves learning, accessibility, and information sharing with a wider audience. with the advent of deep learning and large language models (llms), simplification performance has improved significantly, supported by the release of important datasets @xcite . modern approaches have explored zero-shot prompting @xcite @xcite , instruction tuning (imperial and tayyar madabushi, 2023), and related strategies. from our baseline analysis of trial data, we observed that a larger gap between the cefr level of the original sentence and the target level (cefr-gap) substantially increases the likelihood of simplification failure. this finding highlights the importance of addressing complexity not in a single step but through a structured, iterative process. the primary contribution of this work is to show that multi-round small rule-based simplification are more effective at handling large cefr gaps than conventional single-step approaches. our proposed method mrs-joint outperforms the mrs-rule and baseline models, as validated through extensive experiments and qualitative analyses. additionally, we explored the potential of automatic code generation for text simplification, although further refinement remains necessary. ## task setup the goal of the shared task is to simplify a given source text into a target text with a desired cefr proficiency level (@xmath0. for the datasets, we use the same trial (40 examples) and test @xcite datasets provided by the tsar workshop to build and evaluate our methods. for the evaluation metrics, we follow the same metrics from the official tsar-2025 shared-task metrics, which covers both readability-level control (cefr compliance, we focus on rmse, the distance between predicted and target cefr levels, the lower the better) and meaning preservation(via evaluating semantic fidelity between the simplified sentence and the original sentence, or the simplified sentence and a human-written reference via mean-ingbert @xcite , denoted as mb-orig and mb-ref respectively foot_0 . ## motivation for multi-round in this section, we present our baseline model, the naïve prompt model, and show that simplification becomes increasingly challenging as the gap between the source and target levels widens. ## conclusion we found that a larger gap between the cefr level of the original and target sentences (cefr-gap) increases the likelihood of simplification failure. based on this finding, we proposed two multiround simplification methods generated by gpt-4o: mrs-rule, which applies rule-based simplification, and mrs-joint, which combines rules with prompting. extensive experiments and case studies show that mrs-joint outperforms both the prompting baseline and mrs-rule, confirming the effectiveness of multi-round simplification and the feasibility of text simplifier via code generation.