MuTSE: A Human-in-the-Loop Multi-use Text Simplification Evaluator

ArXi:2604.08947v1 Announce Type: cross As Large Language Models (LLMs) become increasingly prevalent in text simplification, systematically evaluating their outputs across diverse prompting strategies and architectures remains a critical methodological challenge in both NLP research and Intelligent Tutoring Systems (ITS). Developing robust prompts is often hindered by the absence of structured, visual frameworks for comparative text analysis.