OneNRC@TSAR2025 Shared Task Small Models for Readability Controlled Text Simplification

AbstractIn this system description paper, we describe the team OneNRC’s experiments on readability controlled text simplification, focused on using smaller, quantized language models (<20B). We compare these with one large proprietary model and show that the smaller models offer comparable or even better results in some experimental settings. The approach primarily comprises of prompt optimization, agentic workflow, and tool calling. The best results were achieved while using a CEFR proficiency classifier as a verification tool for the language model agent. In terms of comparison with other systems, our submission that used a quantized Gemma3:12B model that ran on a laptop achieved a rank of 9.88 among the submitted systems as per the AUTORANK framework used by the organizers. We hope these results will lead into further exploration on the usefulness of smaller models for text simplification.