🤖 AI Summary
This study addresses the lack of multidimensional, systematic tools for evaluating text simplification by large language models that simultaneously meet research and educational requirements. To bridge this gap, we propose an interactive human-in-the-loop web application enabling parallel simplification and real-time comparative analysis across diverse prompt–model (P×M) configurations, tailored to any target CEFR proficiency level. The core innovation lies in a visualization mechanism that integrates a hierarchical semantic alignment engine with a linear bias heuristic (λ), substantially reducing cognitive load during manual evaluation and facilitating reproducible, structured annotations. The system combines LLM APIs, semantic alignment algorithms, and a responsive front-end framework. Both source code and a live demonstration platform are publicly released, and the tool is readily applicable to downstream NLP dataset construction.
📝 Abstract
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. While researchers typically rely on static computational scripts, educators are constrained to standard conversational interfaces -- neither paradigm supports systematic multi-dimensional evaluation of prompt-model permutations. To address these limitations, we introduce \textbf{MuTSE}\footnote{The project code and the demo have been made available for peer review at the following anonymized URL. https://osf.io/njs43/overview?view_only=4b4655789f484110a942ebb7788cdf2a, an interactive human-in-the-loop web application designed to streamline the evaluation of LLM-generated text simplifications across arbitrary CEFR proficiency targets. The system supports concurrent execution of $P \times M$ prompt-model permutations, generating a comprehensive comparison matrix in real-time. By integrating a novel tiered semantic alignment engine augmented with a linearity bias heuristic ($\lambda$), MuTSE visually maps source sentences to their simplified counterparts, reducing the cognitive load associated with qualitative analysis and enabling reproducible, structured annotation for downstream NLP dataset construction.