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

TL;DR

MuTSE is an interactive web tool that enables systematic, multi-dimensional evaluation of LLM-generated text simplifications across various prompts and models, aiding researchers and educators.

Contribution

It introduces MuTSE, a human-in-the-loop platform that visualizes and compares multiple prompt-model permutations for text simplification evaluation.

Findings

Supports concurrent execution of prompt-model permutations

Provides real-time comparison matrix for simplification outputs

Includes a semantic alignment engine with a linearity bias heuristic

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.