mlsanityData to 14 Oct 2026

SimplifyMyText: An LLM-Based System for Inclusive Plain Language Text Simplification

arXiv 2504.14223 ↗First posted 19 Apr 2025cs.CL cs.AI cs.HCcited by 1

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Abstract (arXiv)

Text simplification is essential for making complex content accessible to diverse audiences who face comprehension challenges. Yet, the limited availability of simplified materials creates significant barriers to personal and professional growth and hinders social inclusion. Although researchers have explored various methods for automatic text simplification, none fully leverage large language models (LLMs) to offer tailored customization for different target groups and varying levels of simplicity. Moreover, despite its proven benefits for both consumers and organizations, the well-established practice of plain language remains underutilized. In this paper, we https://simplifymytext.org, the first system designed to produce plain language content from multiple input formats, including typed text and file uploads, with flexible customization options for diverse audiences. We employ GPT-4 and Llama-3 and evaluate outputs across multiple metrics. Overall, our work contributes to research on automatic text simplification and highlights the importance of tailored communication in promoting inclusivity.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 24 references that are in the map. First posted on arXiv 19 Apr 2025.

Builds on

7 of 24 references.

References · 7

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022LaMDA: Language Models for Dialog Applicationscited by 1,220
  3. 2019Controllable Sentence Simplificationcited by 78
  4. 2020Controllable Text Simplification with Explicit Paraphrasingcited by 53
  5. 2022Evaluating Factuality in Text Simplificationcited by 34
  6. 2021Text Simplification by Taggingcited by 27
  7. 2023Language Models for German Text Simplification: Overcoming Parallel Data Scarcity through Style-specific Pre-trainingcited by 15

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Citing papers per year: 2026: 1.

1 paper.