TriLLaMa at CQs-Gen 2025: A Two-Stage LLM-Based System for Critical Question Generation

This paper presents a new system for generating critical questions in debates, developed for the Critical Questions Generation shared task.Our two-stage approach, combining generation and classification, utilizes LLaMA 3.1 Instruct models (8B, 70B, 405B) with zero-/few-shot prompting.Evaluations on annotated debate data reveal several key insights: fewshot generation with 405B yielded relatively high-quality questions, achieving a maximum possible punctuation score of 73.5.The 70B model outperformed both smaller and larger variants on the classification part.The classifiers showed a strong bias toward labeling generated questions as Useful, despite limited validation.Further, our system, ranked 6 th , outperformed baselines by 3%.These findings stress the effectiveness of large-sized models for question generation and medium-sized models for classification, and suggest the need for clearer task definitions within prompts to improve classification accuracy.