MuTSE: A Human-in-the-Loop Multi-use Text Simplification Evaluator
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}\fo...
| Authors | Rares-Alexandru Roscan, Gabriel Petre1, Adrian-Marius Dumitran et al. |
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| Primary category | cs.CL |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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