Efficient Scalable Methods for Uncovering Critical Interactions in LLMs

Introduction

As Large Language Models (LLMs) become increasingly powerful, understanding their inner workings has become a pressing priority. Interpretability—the science of making model decisions transparent—is essential for building trust, ensuring safety, and debugging unexpected behaviors. However, the sheer scale of modern LLMs introduces a fundamental challenge: model outputs emerge not from individual components, but from intricate interactions among features, training data points, and internal mechanisms. Identifying these interactions in a computationally feasible way is the key to practical interpretability.

The Scalability Challenge in Interpretability

Interpretability research typically takes one of three perspectives: feature attribution, which isolates which input features drive a prediction; data attribution, which links model behavior to influential training examples; and mechanistic interpretability, which dissects internal components. Across all these lenses, the same obstacle appears: complexity grows exponentially with scale. For instance, as the number of input tokens increases, the number of possible pairwise and higher-order interactions skyrockets, making exhaustive analysis impossible. To overcome this, we need algorithms that can efficiently pinpoint the most influential interactions without evaluating every possible combination.

Attribution via Ablation

The core idea behind many attribution methods is ablation—removing or masking a component and observing the change in the model's output. This approach can be applied at multiple levels:

Feature Ablation

In feature attribution, we mask specific segments of the input prompt (e.g., individual words or phrases) and measure the shift in the predicted distribution. The larger the shift, the more that feature contributes to the prediction.

Data Ablation

For data attribution, we train models on different subsets of the training set, effectively “ablating” certain data points. By observing how the model’s output on a test point changes when a training example is removed, we can estimate its influence.

Mechanistic Ablation

In mechanistic interpretability, we intervene on the internal computations of the model—for instance, by zeroing out the contribution of a specific attention head or MLP layer. This reveals which internal structures are responsible for a given behavior.

In all cases, each ablation comes with a significant computational cost, whether from repeated forward passes or full retrainings. Therefore, the goal is to design strategies that require as few ablations as possible to discover the most critical interactions.

The SPEX and ProxySPEX Framework

To address the scalability challenge, we developed SPEX (Scalable Perturbation-based EXplanation) and its more efficient variant ProxySPEX. These algorithms systematically identify pairs or groups of components that interact strongly to influence the model's output. Rather than testing all possible combinations—an infeasible task—SPEX uses a clever sampling and search procedure to zero in on the most impactful interactions.

How SPEX Works

SPEX operates by performing iterative ablation experiments. Starting with a set of candidate components (e.g., input tokens, data points, or internal nodes), it randomly selects subsets to ablate and records the resulting output changes. Through statistical analysis, it identifies combinations where the joint effect differs significantly from the sum of individual effects—indicating an interaction. By focusing on these promising candidates, SPEX dramatically reduces the number of required ablations.

ProxySPEX: Faster Approximations

ProxySPEX takes the core idea further by using a proxy model—a faster, simpler approximation of the LLM—to pre-screen interactions. The proxy model, which can be a smaller network or a linear approximation, is used to generate candidate interactions cheaply. These candidates are then validated with a few precise ablation experiments on the full LLM. This two-stage approach enables discovery of interactions at scales previously thought computationaly intractable.

Practical Implications and Future Directions

The ability to identify interactions at scale opens up new possibilities for LLM debugging, safety auditing, and transparency. For example, it can highlight which training examples jointly bias a model toward harmful outputs, or which internal components collaborate to produce a factually incorrect statement. As models continue to grow, frameworks like SPEX and ProxySPEX will become indispensable tools for interpretability researchers.

In summary, the journey toward trustworthy AI requires methods that can handle the complexity of large-scale interactions. By combining the principled approach of ablation with smart sampling and proxy approximations, we can finally uncover the hidden synergies that drive LLM behavior.