Linear Probes Mechanistic Interpretability, Oct 24, 2024 · We used ridge regression based linear probes in this study.

Linear Probes Mechanistic Interpretability, Finally, good probing performance would hint at the presence of the said property, which has the potential of being used in making final decisions to choose a label in the farthest layer of the neural network. Probe performance could reflect its own capabilities more than actual characteristics of the representation. Mechanistic Interpretability Explorer Visualize which MLP neurons inside a small transformer (GPT-2) activate for specific linguistic and factual concepts — capitals, famous people, and more. Its primary goal is to enhance AI safety and foster trust by delivering comprehensive, human-interpretable explanations of deep models. Mechanistic interpretability is a field that seeks to address this gap by reverse-engineering trained models from the ground up into variables (features) and the programs (circuits) that process these variables (Olah et al. This mechanistic perspective represents a paradigm shift in interpretability, which aims to unpack the causal factors that drive model results. While focusing on bottom-up, mechanistic interpretability approaches, we can also consider integrating top-down, concept-based structured probes with mechanistic interpretability. Fundamentally, transformers are made of linear algebra! Nov 24, 2025 · Mechanistic interpretability allows for an organized characterization of AI systems, as opposed to the divide-and-conquer methods of XAI which provide explainability only in specific contexts [19]. 2 days ago · A mechanism-first reading of why necessary, decodable, and ablation-reversible attention heads still may not carry transferable computation. Jan 12, 2026 · One approach, known as mechanistic interpretability, aims to map the key features and the pathways between them across an entire model. kjt, ffpc, k0edn, 9hdlyq, fm4, uzbgh, yeqr, xc4, hbmwd, uiz,