Mind_Matrix at CQs-Gen 2025: Adaptive Generation of Critical Questions for Argumentative Interventions

To encourage computational argumentation through critical question generation (CQs-Gen), we propose an ACL 2025 CQs-Gen shared task system to generate critical questions (CQs) with the best effort to counter argumentative text by discovering logical fallacies, unjustified assertions, and implicit assumptions.Our system integrates a quantized language model, semantic similarity analysis, and a metaevaluation feedback mechanism including the key stages such as data preprocessing, rationaleaugmented prompting to induce specificity, diversity filtering for redundancy elimination, enriched meta-evaluation for relevance, and a feedback-reflect-refine loop for iterative refinement.Multi-metric scoring guarantees highquality CQs.With robust error handling, our pipeline ranked 7th among 15 teams, outperforming baseline fact-checking approaches by enabling critical engagement and successfully detecting argumentative fallacies.This study presents an adaptive, scalable method that advances argument mining and critical discourse analysis.