RoPE-LIME: RoPE-Space Locality + Sparse-K Sampling for Efficient LLM Attribution

Explaining closed-source Large Language Model (LLM) outputs is challenging because API access prevents gradient-based attribution, while perturbation methods are costly and noisy when they depend on regenerated text. We introduce $\mathbf{\text{Rotary}}\mathbf{\text{Positional}}\mathbf{\text{Embedding}}\mathbf{\text{Linear}}\mathbf{\text{Local}}\mathbf{\text{Interpretable}}\mathbf{\text{Model-agnostic}}\mathbf{\text{Explanations}}\mathbf{\text{(RoPE-LIME)}}$, an open-source extension of gSMILE that decouples reasoning from explanation: given a fixed output from a closed model, a smaller open-source surrogate computes token-level attributions from probability-based objectives (negative log-likelihood and divergence targets) under input perturbations. RoPE-LIME incorporates (i) a locality kernel based on Relaxed Word Mover’s Distance computed in $\mathbf{\text{RoPE}}\mathbf{\text{embedding}}\mathbf{\text{space}}$ for stable similarity under masking, and (ii) $\mathbf{\text{Sparse-}}K$ sampling, an efficient perturbation strategy that improves interaction coverage under limited budgets. Experiments on HotpotQA (sentence features) and a hand-labeled MMLU subset (word features) show that RoPE-LIME produces more informative attributions than leave-one-out sampling and improves over gSMILE while substantially reducing closed-model API calls.