Difficult and unfamiliar concepts often hinder comprehension for lay audiences, especially in technical and educational domains. This motivates the usage of large language models (LLMs) for the process of text simplification (TS). In this work, we propose an iterative refinement framework that aims to simplify definitions by carefully handling complex terminology and domain-specific expressions. The obtained definition is reprocessed based on the critique, making refinements in successive iterations. We emphasize the use of small language models (SLMs) due to their faster response times and cost-efficient deployment. Human evaluations of the definitions produced at each refinement stage indicate consistent improvements in our specified evaluation criteria. We evaluate both LLM-as-a-judge score and human assessments along with automated metrics like BERTScore, BLEU-4, which provided supporting evidence for the effectiveness of our approach. Our work highlights the use of LLMs mimicking human-like feedback system in a TS task catering to a reader’s specific cognitive needs. Thus, we find that an iterative, critique-driven method can be an effective strategy for the simplification of dense or technical texts, particularly in domains where jargon impedes understanding.