Abstract
Recent advances in large language models (LLMs) have demonstrated their potential in handling complex reasoning tasks, which are usually achieved by constructing a thought chain to guide the model to solve the problem with multi-step thinking. However, existing methods often remain confined to previously explored solution spaces and thus overlook the critical blind spot within LLMs' cognitive range. To address these issues, we design the Thought Space Explorer (TSE), a novel framework to expand and optimize thought structures to guide LLMs to explore their blind spots of thinking. By generating new reasoning steps and branches based on the original thought structure with various designed strategies, TSE broadens the thought space and alleviates the impact of blind spots for LLM reasoning. Experimental results on multiple levels of reasoning tasks demonstrate the efficacy of TSE. We also conduct extensive analysis to understand how structured and expansive thought can contribute to unleashing the potential of LLM reasoning capabilities.
Abstract (translated)
大型语言模型（LLMs）在处理复杂推理任务方面的最新进展已经展示了其潜力，这通常是通过构建思维链来引导模型进行多步思考以解决问题。然而，现有方法往往局限于之前探索过的解决方案空间，因此忽视了LLMs认知范围内的关键盲点。为了解决这些问题，我们设计了一个名为思想空间探测器（Thought Space Explorer, TSE）的新框架，旨在扩展和优化思维结构，引导LLMs探索其思维盲点。通过基于原始思维结构生成新的推理步骤和分支，并采用各种设计策略，TSE拓宽了思想空间并减轻了盲点对LLM推理的影响。在多个层次的推理任务上的实验结果证明了TSE的有效性。我们还进行了广泛的分析以理解结构化且扩展性的思维如何有助于释放LLMs的推理潜力。
URL
https://arxiv.org/abs/2410.24155
