Tdnguyen at CQs-Gen 2025: Adapt Large Language Models with Multi-Step Reasoning for Critical Questions Generation
Year: 2025
Type: conference-paper
Abstract: This paper explores the generation of Critical Questions (CQs) from argumentative texts using multi-step reasoning techniques, specifically Chain-of-Thoughts (CoT) and Tree-of-Thoughts (ToT) prompting frameworks.CQs are essential for enhancing critical thinking and improving decision-making across various domains.Despite the promise of Large Language Models (LLMs) in this task, generating contextually relevant and logically sound questions remains a challenge.Our experiments show that CoT-based prompting strategies, including Zero-shot and One-shot methods, significantly outperform baseline models in generating highquality CQs.While ToT prompting offers a more flexible reasoning structure, it was less effective than CoT in this task.We suggest exploring more advanced or computationally intense multi-step reasoning techniques, as well as alternative tree structures for the ToT framework, to further improve CQs-Gen systems.
Authors: Tien-Dat Nguyen, Duc-Vu Nguyen
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412889289