GRADE: Probing Knowledge Gaps in LLMs through Gradient Subspace Dynamics
Detecting whether a model's internal knowledge is sufficient to correctly answer a given question is a fundamental challenge in deploying responsible LLMs. In addition to verbalising the confidence by LLM self-report, more recent methods explore the model internals, such as the hidden states of the response tokens, to capture how much knowledge is activated. We argue that such activated knowledge may not align with what the query requires, e.g., capturing the stylistic and length-related features that are uninformative for answering the query. To fill the gap, we propose GRADE (GRAdient Dynamics for knowlEdge gap detection), which quantifies the knowledge gap via the cross-layer rank rati...
| Authors | Yujing Wang, Yuanbang Liang, Yukun Lai et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 April 2026 |
| Last updated at source | 31 August 2026 |
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