arXiv (All)
open access
Pinocchio: Fast Uncertainty Estimates for Black-Box Language Models
In high-stakes decision-making applications of large language models (LLMs), practitioners require not only accurate LLMs but also uncertainty estimates for their predictions. Existing approaches to uncertainty estimation for LLMs require access to log-probabilities output by the model or require fi
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
- Pinocchio: Fast Uncertainty Estimates for Black-Box Language Models#1028739
- 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
- Prompting the Unknown: Understanding Response Uncertainty in Large Language Models#821661
- Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey#479897
Record · ID 1062665
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