arXiv (All)
open access
BLADE: Distilled LLM Regularization for Calibrated Knowledge Graph Completion
Knowledge graph completion models optimize ranking, although many downstream applications require calibrated probabilities. We present BLADE, a variational model that separates latent truth from graph recording and distills offline language-model judgments into a frozen teacher regularizer. The LLM
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
- Semantic Variational Bayes Based on Semantic Information G Theory for Solving Latent Variables#183160
- A hierarchy of faithfulness criteria for knowledge base completion#1064407
- Extrapolation of Experimental Results through Analogical Reasoning from Latent Classes#774715
- Pattern Over-Generalization of Knowledge Graph Embedding#926403
- GONE: Structural Knowledge Unlearning via Neighborhood-Expanded Distribution Shaping#821807
Record · ID 1077416
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