🤖 AI Summary
This work addresses the limitations of current automatic text simplification approaches, which overly rely on automated metrics that fail to capture users’ actual comprehension abilities and normative standards, thereby offering inadequate support for cognitive accessibility. To overcome this, the authors propose a human-in-the-loop hybrid framework that integrates real-time human guidance during large language model generation alongside post-hoc human oversight. For the first time, this framework systematically embeds human roles into both generation and evaluation phases, leveraging a standards-aligned checklist, an Event-Condition-Action (ECA) rule engine, and accessibility-oriented key performance indicators (KPIs) to enable a traceable, reproducible, and auditable text generation pipeline. Empirical results demonstrate that the approach effectively encodes human feedback, enhances model adaptability, and provides a structured, transparent, and inclusive pathway for evaluating and optimizing accessible texts.
📝 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.