ProxySPEX: Inference-Efficient Interpretability via Sparse Feature Interactions in LLMs

Large Language Models (LLMs) have achieved remarkable performance by capturing complex interactions between input features. To identify these interactions, most existing approaches require enumerating all possible combinations of features up to a given order, causing them to scale poorly with the number of inputs n. Recently, Kang et al. (2025) proposed SPEX, an information-theoretic approach that uses interaction sparsity to scale to n ≈ 10 3 features. SPEX greatly improves upon prior methods but requires tens of thousands of model inferences, which can be prohibitive for large models. In this paper, we observe that LLM feature interactions are often hierarchical-higher-order interactions are accompanied by their lower-order subsets-which enables more efficient discovery. To exploit this hierarchy, we propose PROXYSPEX, an interaction attribution algorithm that first fits gradient boosted trees to masked LLM outputs and then extracts the important interactions. Experiments across four challenging high-dimensional datasets show that PROXYSPEX more faithfully reconstructs LLM outputs by 20% over marginal attribution approaches while using 10× fewer inferences than SPEX. By accounting for interactions, PROXYSPEX efficiently identifies the most influential features, providing a scalable approximation of their Shapley values. Further, we apply PROXYSPEX to two interpretability tasks. Data attribution, where we identify interactions among CIFAR-10 training samples that influence test predictions, and mechanistic interpretability, where we uncover interactions between attention heads, both within and across layers, on a question-answering task. The PROXYSPEX algorithm is available at https://github.com/mmschlk/shapiq.