Tag: Reasoning
All the articles with the tag "Reasoning".
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Toward Reasonable Parrots: Why Large Language Models Should Argue with Us by Design
This position paper advocates for redesigning Large Language Models as 'reasonable parrots' that integrate argumentation theory principles to foster critical thinking through multi-persona dialogues, challenging users with diverse perspectives rather than providing one-sided answers.
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The Promise and Limits of LLMs in Constructing Proofs and Hints for Logic Problems in Intelligent Tutoring Systems
This paper evaluates LLMs in intelligent tutoring systems for propositional logic, demonstrating DeepSeek-V3's promising accuracy in proof construction (up to 86.7%) and hint generation (75%), but reveals significant pedagogical limitations in justification and subgoaling, necessitating hybrid approaches for educational integration.
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MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness
本文提出MAC-Tuning方法,通过分步微调分离答案预测和置信度估计,提升LLMs在多问题设置下的知识边界意识,显著减少幻觉并改善性能。
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Evidence of conceptual mastery in the application of rules by Large Language Models
本文通过心理实验证明大型语言模型在规则应用中表现出概念掌握能力,能够泛化到新情境并部分模仿人类对时间压力等语境的敏感性。
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CRANE: Reasoning with constrained LLM generation
This paper introduces CRANE, a reasoning-augmented constrained decoding algorithm that alternates between unconstrained and constrained generation to preserve LLM reasoning capabilities while ensuring syntactic correctness, achieving up to 10% accuracy improvement on symbolic reasoning benchmarks like GSM-Symbolic and FOLIO.