用推理链引导大模型生成有逻辑依据的批判性问题。
DayDreamer at CQs-Gen 2025: Generating Critical Questions through Argument Scheme Completion
- 基于沃尔顿论证模式，分步构建结构化论点
- 生成多样化且贴合原文的批判性问题，排名精准
- 适合教育、AI辅助思辨训练场景
批判性问题是激发批判性思维的重要资源。本文针对ArgMining 2025的批判性问题生成（CQs-Gen）共享任务，提出基于大语言模型（LLMs）与思维链提示的方法。针对每个输入论点，通过对话式提示让模型先填充沃尔顿论证模式模板，生成结构化论据，再据此生成相关批判性问题。随后，再次利用大模型对所有候选问题进行排序，选出最有助于理解原论点的前3个问题。该方法结合论证理论与逐步推理，在测试集上表现优异，具备促进批判性思维、识别缺失或缺乏依据论断的潜力。代码已公开于：https://git.ecdf.ed.ac.uk/s2236454/DayDreamer-CQs-Gen。
原文摘要 · Abstract (English)
Critical questions are essential resources to provoke critical thinking when encountering an argumentative text. We present our system for the Critical Questions Generation (CQs-Gen) Shared Task at ArgMining 2025. Our approach leverages large language models (LLMs) with chain-of-thought prompting to generate critical questions guided by Walton's argumentation schemes. For each input intervention, we conversationally prompt LLMs to instantiate the corresponding argument scheme template to first obtain structured arguments, and then generate relevant critical questions. Following this, we rank all the available critical questions by prompting LLMs to select the top 3 most helpful questions based on the original intervention text. This combination of structured argumentation theory and step-by-step reasoning enables the generation of contextually relevant and diverse critical questions. Our pipeline achieves competitive performance in the final test set, showing its potential to foster critical thinking given argumentative text and detect missing or uninformed claims. Code available at \href{https://git.ecdf.ed.ac.uk/s2236454/DayDreamer-CQs-Gen}{DayDreamer}.
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