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

MuTSE is an interactive web application designed to evaluate Large Language Model outputs for text simplification tasks across multiple prompting strategies and proficiency levels. The tool addresses a methodological gap in NLP research by providing researchers and educators with a structured, visual framework for comparing prompt-model combinations in real-time.

MuTSE represents a practical contribution to the NLP evaluation landscape, tackling a genuine friction point in LLM assessment workflows. The research community has struggled with systematic evaluation of text simplification outputs, particularly when testing multiple prompt variations against different model architectures. Traditional approaches rely on static computational scripts that lack visual comparative frameworks, while educators remain confined to conversational interfaces—neither approach scales effectively for rigorous multi-dimensional analysis.

The tool's innovation lies in its human-in-the-loop design combined with a tiered semantic alignment engine that reduces cognitive load during qualitative evaluation. By generating comprehensive comparison matrices for P×M prompt-model permutations concurrently, MuTSE enables reproducible annotation workflows essential for constructing robust NLP datasets. The linearity bias heuristic integration suggests thoughtful consideration of how simplification quality degrades across complexity levels.

For the NLP and educational technology sectors, this addresses a bottleneck in model evaluation infrastructure. Researchers developing new simplification approaches gain a standardized evaluation methodology, while intelligent tutoring systems developers can systematically assess which prompt-model combinations best serve learners at specific CEFR proficiency levels. This capability becomes increasingly valuable as organizations deploy specialized LLMs for educational contexts.

The practical impact extends beyond academia. As enterprises implement LLMs for content generation and educational applications, systematic evaluation tools become competitive advantages. Organizations lacking such frameworks risk deploying suboptimal configurations, potentially harming user experience or educational outcomes. The emphasis on reproducibility and structured annotation also facilitates peer review and methodology validation across institutions.

• →MuTSE enables simultaneous evaluation of multiple prompt-model combinations with visual comparative frameworks, eliminating the need for separate computational scripts.
• →The system integrates a novel tiered semantic alignment engine with linearity bias heuristics to reduce cognitive load in qualitative text analysis.
• →Designed for both researchers and educators, supporting CEFR proficiency-level targeting for text simplification tasks.
• →Real-time comparison matrices generated by the tool facilitate reproducible, structured dataset annotation for downstream NLP research.
• →Addresses a methodological gap in evaluating LLM outputs across diverse prompting strategies, improving evaluation rigor in NLP research.