Abstract
Plain Language and Easy-to-Read formats in text simplification are essential for cognitive accessibility. Yet current automatic simplification and evaluation pipelines remain largely automated, metric-driven, and fail to reflect user comprehension or normative standards. This paper introduces a hybrid framework that explicitly integrates human participation into LLM-based accessible text generation. Human-in-the-Loop (HiTL) contributions guide adjustments during generation, while Human-on-the-Loop (HoTL) supervision ensures systematic post-generation review. Empirical evidence from user studies and annotated resources is operationalized into (i) checklists aligned with standards, (ii) Event-Condition-Action trigger rules for activating expert oversight, and (iii) accessibility Key Performance Indicators (KPIs). The framework shows how human-centered mechanisms can be encoded for evaluation and reused to provide structured feedback that improves model adaptation. By embedding the human role in both generation and supervision, it establishes a traceable, reproducible, and auditable process for creating and evaluating accessible texts. In doing so, it integrates explainability and ethical accountability as core design principles, contributing to more transparent and inclusive NLP systems.
Abstract (translated)
Plain Language和Easy-to-Read格式在文本简化中对认知可访问性至关重要。然而，当前的自动简化与评估流程仍高度自动化、指标驱动，未能反映用户理解程度或规范标准。本文提出一个混合框架，将人类参与明确整合到基于大语言模型的可访问文本生成过程中。Human-in-the-Loop（HiTL）贡献在生成过程中指导调整，而Human-on-the-Loop（HoTL）监督则确保系统化的生成后审查。来自用户研究和标注资源的实证证据被转化为：（i）与标准对齐的检查清单；（ii）用于激活专家监督的事件-条件-动作触发规则；（iii）可访问性关键绩效指标（KPIs）。该框架展示了如何将人本机制编码用于评估，并复用其提供结构化反馈以改进模型适应。通过将人类角色嵌入生成与监督双环节，它建立了可追溯、可复现、可审计的可访问文本创建与评估流程。在此过程中，框架将可解释性与伦理问责作为核心设计原则，推动构建更透明、更具包容性的自然语言处理系统。
URL
https://arxiv.org/abs/2603.18879
