Breaking Thought Patterns: A Multi-Dimensional Reasoning Framework for LLMs
This work addresses the limitation of inflexible reasoning in LLMs for generating diverse and creative responses, representing an incremental improvement through a hybrid approach.
The authors tackled the problem of rigid reasoning in large language models by proposing the LADDER framework, which combines Chain-of-Thought reasoning, Mixture of Experts, and multi-dimensional sampling to improve creativity and task performance, with experiments showing significant enhancements in task completion, creativity, and fluency.
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.