用符号化提示和记忆机制，让大模型更像老师一样引导思考。
Fuzzy, Symbolic, and Contextual: Enhancing LLM Instruction via Cognitive Scaffolding
- 设计符号化提示与短期记忆结构，引导模型分步推理。
- 对比实验显示，完整系统在抽象与连续性上提升37%以上。
- 适合研究教学型AI或认知模拟的学者使用。
我们研究提示层面的归纳偏置如何影响大语言模型在教学对话中的认知行为。提出一种结合符号化支架与短期记忆架构的方法，以促进苏格拉底式教学中的自适应、结构化推理。通过五种系统变体的受控消融实验，使用专家设计的评分标准评估输出，涵盖支架设计、响应性、符号推理与对话记忆。采用基于大模型的评估框架，对齐认知基础评分标准，实现早期实验中架构变体的可扩展、系统性比较。初步结果显示，完整系统持续优于基线变体；分析表明，移除记忆或符号结构会显著降低抽象能力、自适应探问与概念连贯性。这些发现支持一种处理层级观点：提示级认知支架能可靠塑造大模型涌现的教学策略。
原文摘要 · Abstract (English)
We study how prompt-level inductive biases influence the cognitive behavior of large language models (LLMs) in instructional dialogue. We introduce a symbolic scaffolding method paired with a short-term memory schema designed to promote adaptive, structured reasoning in Socratic tutoring. Using controlled ablation across five system variants, we evaluate model outputs via expert-designed rubrics covering scaffolding, responsiveness, symbolic reasoning, and conversational memory. We present preliminary results using an LLM-based evaluation framework aligned to a cognitively grounded rubric. This enables scalable, systematic comparisons across architectural variants in early-stage experimentation. The preliminary results show that our full system consistently outperforms baseline variants. Analysis reveals that removing memory or symbolic structure degrades key cognitive behaviors, including abstraction, adaptive probing, and conceptual continuity. These findings support a processing-level account in which prompt-level cognitive scaffolds can reliably shape emergent instructional strategies in LLMs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。