有监督的思维链
Supervised Chain of Thought
AI总结：
本文研究“一提示通用于所有任务”的CoT方法对LLMs可计算性的负面影响，将解空间划分为提示空间和答案空间，并通过实验证明任务特定监督对提升推理性能至关重要。
摘要：
大语言模型（LLMs）已经彻底改变了自然语言处理，并为推动人工智能的发展带来了巨大潜力。然而，大多数主流LLMs的核心架构——Transformer——在计算深度上存在固有局限，使其在理论上无法解决许多需要越来越深计算的推理任务。思维链（Chain of Thought, CoT）提示作为一种应对这些架构局限的技术应运而生，多项理论研究已证明了这一点。它为求解此前超出这些模型能力的复杂推理任务提供了一种有前景的方法。尽管CoT及其变体（如思维树、思维图等）取得了成功，但它们都依赖一种“一提示通用于所有任务”的方法，即对从计数、排序到求解数学和算法问题等广泛任务使用单一提示结构（例如“一步一步思考”）。这种方法给模型生成正确推理步骤带来了显著挑战，因为模型必须在一个庞大的提示模板空间中寻找适合每个任务的模板。在本工作中，我们基于此前对CoT的理论分析，证明这种一提示通用于所有任务的方法会如何对LLMs的可计算性产生负面影响。我们将解搜索空间划分为两部分：提示空间和答案空间。我们的研究结果表明，针对特定任务的监督对于准确导航提示空间并实现最优性能至关重要。通过在目前最先进的LLMs上进行实验，我们揭示了在有无监督情况下推理性能的差距。
英文摘要：
Large Language Models (LLMs) have revolutionized natural language processing and hold immense potential for advancing Artificial Intelligence. However, the core architecture of most mainstream LLMs -- the Transformer -- has inherent limitations in computational depth, rendering them theoretically incapable of solving many reasoning tasks that demand increasingly deep computations. Chain of Thought (CoT) prompting has emerged as a technique to address these architectural limitations, as evidenced by several theoretical studies. It offers a promising approach to solving complex reasoning tasks that were previously beyond the capabilities of these models. Despite its successes, CoT and its variants (such as Tree of Thought, Graph of Thought, etc.) rely on a "one-prompt-for-all" approach, using a single prompt structure (e.g., "think step by step") for a wide range of tasks -- from counting and sorting to solving mathematical and algorithmic problems. This approach poses significant challenges for models to generate the correct reasoning steps, as the model must navigate through a vast prompt template space to find the appropriate template for each task. In this work, we build upon previous theoretical analyses of CoT to demonstrate how the one-prompt-for-all approach can negatively affect the computability of LLMs. We partition the solution search space into two: the prompt space and the answer space. Our findings show that task-specific supervision is essential for navigating the prompt space accurately and achieving optimal performance. Through experiments with state-of-the-art LLMs, we reveal a gap in reasoning performance when supervision is applied versus when it is not.
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。
