arXiv (All)
open access
On Calibration of Large Language Models: From Response To Capability
Accurate confidence estimation is critical for reliable use of large language models (LLMs). Prior work on LLM calibration largely focuses on response-level confidence, which estimates the correctness of a single generated output. However, this formulation is misaligned with many practical settings
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
- A Calibrated Reflection Approach for Enhancing Confidence Estimation in LLMs#927819
- 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
- Pinocchio: Fast Uncertainty Estimates for Black-Box Language Models#1062665
- Pinocchio: Fast Uncertainty Estimates for Black-Box Language Models#1028739
Record · ID 1131467
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