Semantic Token Clustering for Efficient Uncertainty Quantification in Large Language Models

📰 ArXiv cs.AI

Semantic token clustering enables efficient uncertainty quantification in large language models

advanced Published 23 Mar 2026

Action Steps

Identify the need for uncertainty quantification in LLMs
Apply semantic token clustering to reduce computational overhead
Evaluate the effectiveness of the method in improving model reliability

Who Needs to Know This

ML researchers and engineers working on LLMs can benefit from this method to improve model reliability, while data scientists and AI engineers can apply it to various NLP tasks

Key Insight

💡 Semantic token clustering can efficiently quantify uncertainty in LLMs without substantial computational overhead

Key Takeaways

Semantic token clustering enables efficient uncertainty quantification in large language models

Full Article

Title: Semantic Token Clustering for Efficient Uncertainty Quantification in Large Language Models

Abstract:
arXiv:2603.20161v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across diverse tasks. However, the truthfulness of their outputs is not guaranteed, and their tendency toward overconfidence further limits reliability. Uncertainty quantification offers a promising way to identify potentially unreliable outputs, but most existing methods rely on repeated sampling or auxiliary models, introducing substantial computational overhead. To address

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