Natural Language Explanations (NLEs) describe how Large Language Models (LLMs) make decisions by drawing on external Context Knowledge (CK) and Parametric Knowledge (PK). Understanding the interaction between these sources is key to assessing NLE grounding, yet these dynamics remain underexplored. Prior work has largely focused on (1) single-step generation and (2) modelled PK-CK interaction as a binary choice within a rank-1 subspace. This approach overlooks richer interactions and how they unfold over longer generations, such as complementary or supportive knowledge. We propose a novel rank-2 projection subspace that disentangles PK and CK contributions more accurately and use it for the first multi-step analysis of knowledge interactions across longer NLE sequences. Experiments across four QA datasets and three open-weight LLMs demonstrate that while rank-1 subspaces struggle to represent diverse interactions, our rank-2 formulation captures them effectively, highlighting PK alignment for supportive interactions and CK alignment for conflicting ones. Our multi-step analysis reveals, among others, that hallucinated generations exhibit strong alignment with the PK direction, whereas context-faithful generations maintain a more balanced alignment between PK and CK.

翻译：自然语言解释通过利用外部语境知识和参数化知识来描述大语言模型的决策过程。理解这两种知识源之间的交互是评估自然语言解释基础性的关键，然而这些动态机制仍未得到充分探索。先前研究主要集中于（1）单步生成，以及（2）在秩-1子空间中将参数化知识与语境知识的交互建模为二元选择。这种方法忽略了更丰富的交互模式及其在生成长序列过程中的演变，例如互补性或支持性知识。我们提出了一种新颖的秩-2投影子空间，能够更精确地解耦参数化知识与语境知识的贡献，并首次将其用于跨长序列自然语言解释的多步知识交互分析。在四个问答数据集和三个开源权重大语言模型上的实验表明：秩-1子空间难以表征多样化的知识交互，而我们的秩-2建模方法能有效捕捉这些交互，突显支持性交互中参数化知识的对齐性以及冲突性交互中语境知识的对齐性。我们的多步分析进一步揭示，幻觉生成结果表现出与参数化知识方向的强对齐性，而语境可信的生成结果则保持参数化知识与语境知识间更均衡的对齐关系。