constructing-llm-probes

What problem does it solve?

Probes provide a structured way to uncover what information is encoded in LLM activations, enabling researchers to assess interpretability, compare models, and localize signals across layers and spans.

Core Features & Use Cases

• Activation extraction across layers with output_hidden_states
• Train linear and nonlinear probes (Logistic Regression, Ridge, MLP) to test hypotheses
• Analysis tools: PCA, SAE, logit lens, and causal interventions
• Use cases: determine what information (syntax, sentiment, facts) is captured, compare models, and localize signals in specific layers/spans

Quick Start

Run a minimal probing workflow to extract hidden states, train a linear or nonlinear probe, and evaluate across layers.