Truth Anchoring (TAC) provides a post-hoc calibration method to align LLM uncertainty metrics with actual factual correctness.
April 2, 2026
Original Paper
Towards Reliable Truth-Aligned Uncertainty Estimation in Large Language Models
arXiv · 2604.00445
The Takeaway
Demonstrates that current uncertainty metrics fail in low-information regimes because they are not grounded in truth. TAC maps raw scores to 'truth-aligned' scores, providing a more reliable protocol for detecting hallucinations.
From the abstract
Uncertainty estimation (UE) aims to detect hallucinated outputs of large language models (LLMs) to improve their reliability. However, UE metrics often exhibit unstable performance across configurations, which significantly limits their applicability. In this work, we formalise this phenomenon as proxy failure, since most UE metrics originate from model behaviour, rather than being explicitly grounded in the factual correctness of LLM outputs. With this, we show that UE metrics become non-discri