Right at My Level: A Unified Multilingual Framework for Proficiency-Aware Text Simplification

• URL: http://arxiv.org/abs/2604.05302v2
• Date: Thu, 16 Apr 2026 02:34:44 GMT
• Title: Right at My Level: A Unified Multilingual Framework for Proficiency-Aware Text Simplification
• Abstract summary: Existing large language model (LLM)-based readability control methods rely on pre-labeled sentence corpora and primarily target English.<n>We propose Re-RIGHT, a unified reinforcement learning framework for adaptive multilingual text simplification without parallel corpus supervision.
• Score: 21.371851241567626
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text simplification supports second language (L2) learning by providing comprehensible input, consistent with the Input Hypothesis. However, constructing personalized parallel corpora is costly, while existing large language model (LLM)-based readability control methods rely on pre-labeled sentence corpora and primarily target English. We propose Re-RIGHT, a unified reinforcement learning framework for adaptive multilingual text simplification without parallel corpus supervision. We first show that prompting-based lexical simplification at target proficiency levels (CEFR, JLPT, TOPIK, and HSK) performs poorly at easier levels and for non-English languages, even with state-of-the-art LLMs such as GPT-5.2 and Gemini 2.5. To address this, we collect 43K vocabulary-level data across four languages (English, Japanese, Korean, and Chinese) and train a compact 4B policy model using Re-RIGHT, which integrates three reward modules: vocabulary coverage, semantic preservation, and coherence. Compared to the stronger LLM baselines, Re-RIGHT achieves higher lexical coverage at target proficiency levels while maintaining original meaning and fluency.

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