Simplifying Administrative Texts for Plain Language using LLM: a Model Comparative Analysis - Holanda, João Pedro; Magalhães, Regis; Almendra, Camilo; Rêgo, Luis Gustavo Coutinho do (Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI), 2026)
Research Context: Plain language is a growing tendency to create more inclusive and readable documents. Open and closed LLMs can be used to simplify documents, with varying costs and tradeoffs. Scientific and/or Practical Problem: Currently, the UFC Inova institution manually writes simplified versions of their public notices, causing longer publication time and repetitive effort. Proposed Solution and/or Analysis: We design a LLM-based pipeline that generates simplified versions of public notices with zero-shot prompting following plain language directives. We investigate how open LLMs compare to proprietary models. Although proprietary solutions are the cutting-edge models, adoption of open LLMs lowers the cost of ownership and avoids vendor lock-in, making them a sustainable choice for public-sector universities. Related IS Theory: We frame this work as a Socio-Technical Systems intervention to enhance access to public information, grounding it in prior research on text complexity and plain-language communication. Research Method: We evaluated a text simplification pipeline applied against different LLMs. The original and AI-generated versions were compared using statistical readability indexes and morphosyntactic metrics. Afterwards, two pairs of documents were evaluated in a survey with readers’ representatives. Summary of Results: The quantitative evaluations indicate that Gemini Flash outperformed the Pro version on both set of metrics, and the Qwen2.5:14b open model was closest to both in the morphosyntactic aspect. Regarding the qualitative evaluation, we observed that automated simplification was well received, but it may better support readers when combined with summarization. Contributions and Impact to IS area: This work provides a process to foster the adoption of plain language in the public sector, along with empirical evidence on the effectiveness of open LLMs compared to proprietary models.