arXiv (OAI)
open access
Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models
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
- Do Large Language Models Capture the Diversity in their Training Data?#924174
- Do Large Language Models Capture the Diversity in their Training Data?#919943
- Language models suffer from a curse of ambiguity#616614
- Language models suffer from a curse of ambiguity#616114
- Improving Semantic Uncertainty Quantification in LVLMs with Semantic Gaussian Processes#987527
Record · ID 177037
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