发现大模型内部共享逻辑空间，用双视角引导推理更准
Discovering a Shared Logical Subspace: Steering LLM Logical Reasoning via Alignment of Natural-Language and Symbolic Views
- 通过分析自然语言与符号语言激活差异，找到共享逻辑子空间
- 无需训练，推理准确率最高提升11个百分点，跨领域泛化好
- 适合想提升模型逻辑推理能力的研究者和开发者
大型语言模型在多步逻辑推理上仍表现不佳。现有方法或仅优化自然语言推理链，或外接符号求解器。本文提出：大模型中是否存在一个同时对齐自然语言与符号语言推理视图的共享内部逻辑子空间？我们假设该子空间捕获了不依赖表面形式的通用逻辑能力。通过在自然语言与符号语言推理链的残差激活对上应用典型相关分析，学习出具有最大跨视图相关性的低维子空间。进一步设计了一种无需训练的方法，沿此逻辑子空间引导模型推理，从而融合双视角的互补信号。在四个逻辑推理基准上的实验表明，该方法显著提升性能，准确率最高提升11个百分点，且在跨领域问题上具有良好泛化能力。
原文摘要 · Abstract (English)
Large Language Models (LLMs) still struggle with multi-step logical reasoning. Existing approaches either purely refine the reasoning chain in natural language form or attach a symbolic solver as an external module. In this work, we instead ask whether LLMs contain a shared internal logical subspace that simultaneously aligns natural-language and symbolic-language views of the reasoning process. Our hypothesis is that this logical subspace captures logical reasoning capabilities in LLMs that are shared across views while remaining independent of surface forms. To verify this, we employ Canonical Correlation Analysis on the paired residual activations from natural-language and symbolic-language reasoning chains, learning a low-dimensional subspace with maximum cross-view correlation. Furthermore, we design a training-free approach that steers LLMs reasoning chain along this logical subspace, thereby leveraging the complementary reasoning signals from both views. Experiments on four logical reasoning benchmarks demonstrate the effectiveness of our approach, improving accuracy by up to 11 percentage points and generalizing well on out-of-domain problems.
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