用大模型生成并筛选批判性问题，提升对话中的深度思考能力。
ELLIS Alicante at CQs-Gen 2025: Winning the critical thinking questions shared task: LLM-based question generation and selection
- 两阶段框架：先生成候选问题，再由模型筛选相关度高的
- 在ACL 2025共享任务中排名第一，验证了方法有效性
- 适合关注批判性思维训练与对话系统改进的研究者
基于大语言模型（LLM）的聊天界面广泛普及，但可能助长浅层学习，削弱批判性思维发展。本文探索利用LLM生成挑战未经证实或模糊论断的批判性问题，以促进深层推理。本研究是第12届论点挖掘研讨会（ACL 2025）举办的共享任务之一，聚焦自动批判性问题生成。我们提出一个两步框架，使用两个小型开源语言模型：Questioner负责生成多个候选问题，Judge负责筛选最相关的。该系统在竞赛中排名第一，证明了所提方法在鼓励对论辩文本进行批判性互动方面的潜力。
原文摘要 · Abstract (English)
The widespread adoption of chat interfaces based on Large Language Models (LLMs) raises concerns about promoting superficial learning and undermining the development of critical thinking skills. Instead of relying on LLMs purely for retrieving factual information, this work explores their potential to foster deeper reasoning by generating critical questions that challenge unsupported or vague claims in debate interventions. This study is part of a shared task of the 12th Workshop on Argument Mining, co-located with ACL 2025, focused on automatic critical question generation. We propose a two-step framework involving two small-scale open source language models: a Questioner that generates multiple candidate questions and a Judge that selects the most relevant ones. Our system ranked first in the shared task competition, demonstrating the potential of the proposed LLM-based approach to encourage critical engagement with argumentative texts.
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