Multi-Step Knowledge Interaction Analysis via Rank-2 Subspace
  Disentanglement
Natural Language Explanations (NLEs) describe how Large Language Models
(LLMs) make decisions, drawing on both external Context Knowledge (CK) and
Parametric Knowledge (PK) stored in model weights. Understanding their
interaction is key to assessing the grounding of NLEs, yet it remains
underexplored. Prior work has largely examined only single-step generation,
typically the final answer, and has modelled PK and CK interaction only as a
binary choice in a rank-1 subspace. This overlooks richer forms of interaction,
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 on four QA datasets and three open-weight
instruction-tuned LLMs show that diverse knowledge interactions are poorly
represented in a rank-1 subspace but are effectively captured in our rank-2
formulation. Our multi-step analysis reveals that hallucinated NLEs align
strongly with the PK direction, context-faithful ones balance PK and CK, and
Chain-of-Thought prompting for NLEs shifts generated NLEs toward CK by reducing
PK reliance. This work provides the first framework for systematic studies of
multi-step knowledge interactions in LLMs through a richer rank-2 subspace
disentanglement. Code and data:
https://github.com/copenlu/pk-ck-knowledge-disentanglement.
690a048fd70e173c84528ddf
Sekh Mainul Islam
claimed_verified
2025-11-06T11:48:37.348Z
637390430938c0754238276c
https://cdn-avatars.huggingface.co/v1/production/uploads/637390430938c0754238276c/O7IIJdZsS9yjznh780XwA.jpeg
Sekh Mainul Islam
sekhcopenlu
user
sekhcopenlu
690a048fd70e173c84528de0
Pepa Atanasova
690a048fd70e173c84528de1
Isabelle Augenstein
Multi-Step Knowledge Interaction Analysis via Rank-2 Subspace
  Disentanglement
Natural Language Explanations (NLEs) describe how Large Language Models
(LLMs) make decisions, drawing on both external Context Knowledge (CK) and
Parametric Knowledge (PK) stored in model weights. Understanding their
interaction is key to assessing the grounding of NLEs, yet it remains
underexplored. Prior work has largely examined only single-step generation,
typically the final answer, and has modelled PK and CK interaction only as a
binary choice in a rank-1 subspace. This overlooks richer forms of interaction,
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 on four QA datasets and three open-weight
instruction-tuned LLMs show that diverse knowledge interactions are poorly
represented in a rank-1 subspace but are effectively captured in our rank-2
formulation. Our multi-step analysis reveals that hallucinated NLEs align
strongly with the PK direction, context-faithful ones balance PK and CK, and
Chain-of-Thought prompting for NLEs shifts generated NLEs toward CK by reducing
PK reliance. This work provides the first framework for systematic studies of
multi-step knowledge interactions in LLMs through a richer rank-2 subspace
disentanglement. Code and data:
https://github.com/copenlu/pk-ck-knowledge-disentanglement.
A novel rank-2 projection subspace is proposed to analyze multi-step knowledge interactions in Large Language Models, revealing how Parametric Knowledge and Context Knowledge contribute to Natural Language Explanations.
Large Language Models Natural Language Explanations Context Knowledge Parametric Knowledge rank-2 projection subspace multi-step analysis knowledge interactions hallucinated NLEs context-faithful NLEs Chain-of-Thought prompting