Categories: FAANG

Trained on Tokens, Calibrated on Concepts: The Emergence of Semantic Calibration in LLMs

Large Language Models (LLMs) often lack meaningful confidence estimates for their outputs. While base LLMs are known to exhibit next-token calibration, it remains unclear whether they can assess confidence in the actual meaning of their responses beyond the token level. We find that, when using a certain sampling-based notion of semantic calibration, base LLMs are remarkably well-calibrated: they can meaningfully assess confidence in open-domain question-answering tasks, despite not being explicitly trained to do so. Our main theoretical contribution establishes a mechanism for why semantic…

Semantic Mastery: Enhancing LLMs with Advanced Natural Language Understanding

Large language models (LLMs) have greatly improved their capability in performing NLP tasks. However, deeper semantic understanding, contextual coherence, and more subtle reasoning are still difficult to obtain. The paper discusses state-of-the-art methodologies that advance LLMs with more advanced NLU techniques, such as semantic parsing, knowledge integration, and contextual reinforcement…

December 10, 2025

In "FAANG"

The Calibration Generalization Gap

This paper was accepted at the Workshop on Distribution-Free Uncertainty Quantification at ICML 2022. Calibration is a fundamental property of a good predictive model: it requires that the model predicts correctly in proportion to its confidence. Modern neural networks, however, provide no strong guarantees on their calibration— and can be…

October 19, 2022

In "FAANG"