HIT - YOU at TSAR 2025 Shared Task Leveraging Similarity-Based Few-Shot Prompting, Round-Trip Translation, and Self-Refinement for Readability-Controlled Text Simplification (2025)

Abstract: We describe our submission to the TSAR 2025 shared task on readability-controlled text simplification, which evaluates systems on their ability to adjust linguistic complexity to specified CEFR levels while preserving meaning and coherence. We explored two complementary frameworks leveraging the shared task CEFR classifier as feedback. The first is an ensemble approach generating diverse candidates using multiple LLMs under zero-shot prompting with level-specific instructions and vocabulary lists, one-shot prompting, and round-trip translation. Candidates were filtered by predicted CEFR level before an LLM judge selected the final output. The second framework is a self-refinement loop, where a single candidate is iteratively revised with classifier feedback until matching the target level or reaching a maximum number of iterations. This study is among the first to apply round-trip translation and iterative self-refinement to controlled simplification, broadening the toolkit for adapting linguistic complexity.

## introduction text simplification aims to reduce the complexity of text while preserving meaning, thereby improving accessibility for language learners and readers with limited proficiency. the tsar 2025 shared task @xcite focuses on readability-controlled simplification, where english passages at cefr level b2 or above must be rewritten to a specified target level (a1, a2, or b1). systems are evaluated on cefr compliance, semantic similarity to the original text and references, with the challenge that no parallel training data is provided. previous research has explored generation mechanisms that encourage structural and lexical reformulation that might facilitate controllable simplification. cross-lingual and translation-based pipelines, for example, have been shown to induce diverse edits such as synonym substitutions and word reorderings @xcite , effects that can be leveraged to achieve finer readability control. in another line of work, iterative refinement frameworks such as self-refine @xcite improve llm outputs through self-feedback guided by external evaluators, offering a general way of enforcing generation constraints such as readability control. building on these insights, we explore two frameworks for readability-controlled simplification, both guided by the shared-task cefr classifier (figure 1 ). ## results when considering only the best run per team, our team ranks third out of 20 teams overall. our gpt-5 and gemini ensemble runs achieve autorank foot_2 values of 3.61 and 3.67, respectively, among 48 submitted runs, demonstrating competitive performance across all official metrics. the self-refinement run ranks at @xmath0.46, showing slightly weaker cefr control and meaning preservation, but operates with much lower computational cost, relying on a single model rather than multi-model ensembling and repeated generations. ## discussion effectiveness of ensemble we demonstrate the contribution of the ensemble process by comparing its performance against the strongest singlemodel baselines, shown in the upper part of ## conclusion and future work we presented three systems for the tsar 2025 shared task on readability-controlled text simplification: two ensemble pipelines that combine diverse generators with an llm-as-a-judge, and a self-refinement loop guided by classifier feedback. in developing the ensemble system, we explored multiple prompting strategies, including style-and vocabulary-based instruction, similarity-based fewshot prompting, and round-trip translation. our analysis highlights systematic differences in how current llms interpret and operationalize level-control instructions, as well as the effects of incorporating demonstrations and classifier-guided feedback. overall, the results indicate that llms under our frameworks form a strong foundation for controllable simplification, but that the trade-off between reliable level control and meaning preservation remains unresolved. looking ahead, there are several directions for strengthening our two frameworks under the current evaluation setting. for the ensemble, future work could focus on developing more principled aggregation strategies and analyzing the decision behavior of llm-as-a-judge models, particularly how they balance readability against meaning when selecting outputs. for the self-refinement framework, richer feedback signals beyond classifier guidance (e.g., semantic similarity, stylistic alignment) could help stabilize convergence and better preserve meaning. at the same time, our observations during system development hinted that the current automatic evaluation may be brittle. small stylistic or structural variations can produce large metric shifts, even when readability and meaning remain comparable to human readers. for example, we observed that formatting differences such as line breaks could noticeably affect classifier predictions (appendix f). revisiting evaluation design to include human-or reader-centered assessments would therefore provide a more reliable view of simplification quality and practical usefulness.