Identifying interactions at scale for LLMs

2026-04-10 · Source: ΑΙhub · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, medium

Summary

The SPEX (Spectral Explainer) and ProxySPEX frameworks are novel algorithms designed to identify influential interactions within complex machine learning systems, particularly Large Language Models (LLMs), at scales orders of magnitude greater than previous methods. These frameworks address the challenge of complexity at scale in interpretability research by leveraging principles of sparsity and low-degreeness in influential interactions, reframing the problem as sparse recovery. ProxySPEX further enhances efficiency by exploiting hierarchical structures, reducing the number of required ablations by approximately 10x. The methods are demonstrated across feature attribution, identifying complex relationships like double negatives or RAG task synthesis; data attribution, distinguishing synergistic and redundant training data interactions; and model component attribution, revealing structural dependencies between attention heads, even improving model performance through informed pruning on an MMLU dataset.

Key takeaway

For research scientists focused on LLM interpretability, SPEX and ProxySPEX offer a scalable approach to understanding complex model behaviors. You should consider integrating these frameworks, available in the SHAP-IQ repository, to analyze feature, data, and model component interactions, especially for long-context inputs or when optimizing model architecture through pruning. This can lead to more trustworthy AI and improved model performance.

Key insights

SPEX and ProxySPEX efficiently identify influential interactions in LLMs by exploiting sparsity and hierarchy.

Principles

• Model behavior arises from complex dependencies.
• Influential interactions are sparse and low-degree.
• Higher-order interactions imply lower-order subset importance.

Method

SPEX uses strategically selected ablations and efficient decoding from signal processing to disentangle combined signals. ProxySPEX adds hierarchical property exploitation for 10x fewer ablations.

In practice

• Identify specific symptoms driving medical diagnoses.
• Uncover redundant or synergistic training data points.
• Inform task-specific attention head pruning for performance gains.

Topics

• LLM Interpretability
• SPEX Framework
• ProxySPEX Algorithm
• Feature Attribution
• Data Attribution

Code references

• mmschlk/shapiq

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Ethicist

Related on AIssential

• Identifying Interactions at Scale for LLMs — SPEX and ProxySPEX efficiently identify influential interactions in ML models by exploiting sparsity and hierarchy.
• Proxy-Based Approximation of Shapley and Banzhaf Interactions — ProxySHAP improves Shapley and Banzhaf interaction estimation by combining tree-based proxies with residual correction for speed and accuracy.
• A Gentle Primer on LLM Explainability — LLM explainability requires dynamic evaluation and model-agnostic methods to understand why models generate specific outputs, moving beyond simple correctness.
• SAILS: Surrogate-based Analysis of Interactions via Local Effect Smooths — SAILS characterizes pairwise feature interactions in black-box models by fitting GAM surrogates to local effects, enabling detection, categorization, and visualization of functional forms.
• The Representational Limit of Scalar Interactions: An Interventional Decomposition — Scalar interaction scores inherently conflate uniqueness, redundancy, and synergy, necessitating advanced decomposition for accurate feature attribution.
• From Sparse Features to Trustworthy Proxies: Certifying SAE-Based Interpretability — A framework certifies SAE-based LM interpretability by bounding risk through a sparse proxy's fidelity and complexity.

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Editorial summary, takeaway, and curation by AIssential. Original article published by ΑΙhub.