Large language models (LLMs) enable strong text generation, and in general there is a practical tradeoff between fine-tuning and prompt engineering. We introduce Simplify-This, a comparative study evaluating both paradigms for text simplification with encoder-decoder LLMs across multiple benchmarks, using a range of evaluation metrics. Fine-tuned models consistently deliver stronger structural simplification, whereas prompting often attains higher semantic similarity scores yet tends to copy inputs. A human evaluation favors fine-tuned outputs overall. We release code, a cleaned derivative dataset used in our study, checkpoints of fine-tuned models, and prompt templates to facilitate reproducibility and future work.
@article{arxiv.2601.05794,
title = {Simplify-This: A Comparative Analysis of Prompt-Based and Fine-Tuned LLMs},
author = {Eilam Cohen and Itamar Bul and Danielle Inbar and Omri Loewenbach},
journal= {arXiv preprint arXiv:2601.05794},
year = {2026}
}