Springer Nature OA
open access
Enhancing AI Interpretability Through Localised Architectures
Recent advances in generative AI, especially powerful Large Language Models (LLMs), raise concerns over the interpretability, safety and sustainability of these large and opaque AI models. The power of such architectures is derived not only from the scalability of deep neural networks (DNNs), but al
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- LLM Layers Immediately Correct Each Other#985288
- SPICE: Simple Polysemantic Feature Interpretation via Clustering-based Explanation#1071005
- Scalable Circuit Learning for Interpreting Large Language Models#1296443
- Cross-Layer Discrete Concept Discovery for Interpreting Language Models#1142543
- HARMONIA: Interpretable Graph Learning through Mixtures of Neural Bases#1119045
Record · ID 1317919
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive