arXiv (All)
open access
Towards Reliable Truth-Aligned Uncertainty Estimation in Large Language Models
Uncertainty estimation (UE) aims to quantify uncertainty in large language model (LLM) outputs to flag potential hallucinations and support reliable deployment. However, UE metrics often exhibit unstable performance across models and datasets, limiting their practical utility. We formalise this phen
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
- Comparing Uncertainty Measurement and Mitigation Methods for Large Language Models: A Systematic Review#670332
- Comparing Uncertainty Measurement and Mitigation Methods for Large Language Models: A Systematic Review#668267
- SpanUQ: Span-Level Uncertainty Quantification for Large Language Model Generation#1142737
- Label-Confidence-Aware Uncertainty Estimation in Natural Language Generation#1123914
- Revealing Epistemic Uncertainty in MLLMs via Causal-Invariant Masking#1187565
Record · ID 1387018
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