打破思维定式：面向大语言模型的多维度推理框架
Breaking Thought Patterns: A Multi-Dimensional Reasoning Framework for LLMs
AI总结：
针对大语言模型推理僵化、缺乏创造性的问题，提出融合思维链推理、混合专家模型与多维度采样的LADDER框架，实验证明其在任务完成度、创造性等方面优于传统模型。
摘要：
大语言模型（LLM）常受僵化推理过程的制约，限制了其生成创造性、多样性回复的能力。为解决这一问题，本文提出了名为LADDER的新型框架，结合思维链（Chain-of-Thought, CoT）推理、混合专家（Mixture of Experts, MoE）模型与多维度上/下采样策略，突破了传统LLM的局限性。首先，CoT推理引导模型完成多步逻辑推理，拓展语义空间，打破思维僵化。其次，MoE将推理任务分配给多个专家模块，每个模块专注于特定子任务。最后，降维操作将推理输出映射回低维语义空间，生成更精准且更具创造性的回复。在多个任务上开展的大量实验表明，LADDER显著提升了任务完成度、创造性与流畅度，能生成兼具创新性与连贯性的回复，表现优于传统模型。消融实验揭示了CoT与MoE在增强推理能力与创造性输出方面的关键作用。本工作推动了更灵活、更具创造力的LLM发展，这类模型能够应对复杂的新型任务。
英文摘要：
Large language models (LLMs) are often constrained by rigid reasoning processes, limiting their ability to generate creative and diverse responses. To address this, a novel framework called LADDER is proposed, combining Chain-of-Thought (CoT) reasoning, Mixture of Experts (MoE) models, and multi-dimensional up/down-sampling strategies which breaks the limitations of traditional LLMs. First, CoT reasoning guides the model through multi-step logical reasoning, expanding the semantic space and breaking the rigidity of thought. Next, MoE distributes the reasoning tasks across multiple expert modules, each focusing on specific sub-tasks. Finally, dimensionality reduction maps the reasoning outputs back to a lower-dimensional semantic space, yielding more precise and creative responses. Extensive experiments across multiple tasks demonstrate that LADDER significantly improves task completion, creativity, and fluency, generating innovative and coherent responses that outperform traditional models. Ablation studies reveal the critical roles of CoT and MoE in enhancing reasoning abilities and creative output. This work contributes to the development of more flexible and creative LLMs, capable of addressing complex and novel tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
