6 Essential Things You Need to Know About LLMs and Interaction Detection at Scale

Large Language Models (LLMs) have revolutionized artificial intelligence, but understanding how they make decisions remains a formidable challenge. As these models grow in complexity, their behavior emerges from intricate interactions among countless features, training examples, and internal components. This article breaks down the core concepts behind identifying those interactions at scale, from attribution methods to advanced frameworks like SPEX and ProxySPEX. Here are six key insights that demystify this cutting-edge field.

1. The Scale Problem in LLM Interpretability

Modern LLMs operate with billions of parameters, processing inputs that can span thousands of tokens. The sheer volume creates a combinatorial explosion: the number of potential interactions between features, data points, or components grows exponentially with size. Traditional interpretability methods that analyze individual elements in isolation fall short because they miss the collaborative effects that drive model outputs. For instance, a prediction might depend on the combined presence of specific words in the prompt, not just each word alone. Addressing this scale problem is the first step toward building trustworthy AI systems that can handle real-world complexity.

2. Three Lenses for Viewing Model Behavior

Interpretability research typically examines LLMs from three distinct perspectives. Feature attribution isolates which input features—like tokens or phrases—most influence a prediction (Lundberg & Lee, 2017; Ribeiro et al., 2022). Data attribution links model outputs to specific training examples that shaped its knowledge (Koh & Liang, 2017; Ilyas et al., 2022). Mechanistic interpretability dissects internal components, such as attention heads or neurons, to understand their functional roles (Conmy et al., 2023; Sharkey et al., 2025). Each lens offers unique insights, but all must grapple with interactions: features combine, data points influence each other, and components work together. Recognizing these interdependencies is crucial for a holistic understanding.

3. Attribution Through Ablation as a Core Technique

A fundamental approach to measuring influence is ablation—removing a component and observing the change in output. For feature attribution, we mask parts of the input prompt and record prediction shifts. For data attribution, we train models on different subsets of the training set, noting how outputs vary when certain examples are omitted. For mechanistic interpretability, we intervene directly on the forward pass, nullifying the effect of specific internal modules. In each case, the goal is to isolate drivers of decisions by systematically perturbing the system. However, each ablation incurs significant computational cost—whether through repeated inference calls or full retraining—making efficiency a top priority.

4. The Need for Interaction-Centric Methods

State-of-the-art LLMs achieve their performance by synthesizing complex relationships. A feature’s impact often depends on other features; a training example’s influence may be synergistic with others; internal components can gate or amplify each other’s signals. Exhaustively testing all interactions is computationally infeasible—the number of pairs alone can reach billions. Therefore, interpretability methods must be designed to detect critical interactions without enumerating all possibilities. This requires algorithms that can identify which combinations matter most, based on the model’s behavior, rather than brute-force search. The challenge is to balance thoroughness with tractability, extracting meaningful insights while keeping compute budgets realistic.

5. Introducing SPEX: Scalable Interaction Discovery

The SPEX framework (Sharkey et al., 2025) tackles the interaction problem head-on. It formulates the search for influential interactions as a combinatorial optimization task, using ablation-based measurements to evaluate candidate interactions efficiently. Instead of testing all pairs or groups, SPEX leverages sparsity—assuming that only a small fraction of interactions truly matter—and uses techniques like greedy selection or proxy models to converge quickly. This makes it possible to discover key interactions among features, data points, or components even in massive-scale models. SPEX demonstrates that with clever algorithmic design, we can cut through the combinatorial explosion and pinpoint the dependencies that drive model behavior.

6. ProxySPEX: Speeding Up with Predictive Proxies

While SPEX reduces the number of required ablations, each ablation still involves running the LLM—potentially expensive. ProxySPEX accelerates this by replacing the actual model with a proxy: a simpler, cheaper-to-evaluate model that approximates the original’s response to ablations. The proxy is trained on a sparse set of ablation examples, learning to predict outcomes for unseen ablated configurations. ProxySPEX then runs the interaction search using the proxy, drastically cutting computational costs. The catch is that the proxy must be accurate enough to preserve the discovered interactions. This trade-off between speed and fidelity is carefully managed, making ProxySPEX suitable for routine interpretability audits.

Together, these six points illuminate the path forward for LLM interpretability at scale. From recognizing the exponential complexity of interactions to leveraging ablations and proxy models, researchers are building tools that can keep pace with the models themselves. The ultimate goal is not just to understand LLMs, but to ensure they are safe, transparent, and aligned with human values. By focusing on interactions, we move one step closer to AI systems we can trust.