LogitScope: A Framework for Analyzing LLM Uncertainty Through Information Metrics

2026-03-25

AI Summary

Existing approaches struggle to quantify the uncertainty of large language models (LLMs) at a fine-grained, token-level during generation, limiting our understanding of model confidence and hallucination mechanisms. This work proposes LogitScope, a lightweight, unsupervised, and annotation-free framework that computes information-theoretic metrics—such as entropy and variance entropy—over token-level probability distributions in real time during inference. Model-agnostic and computationally efficient, LogitScope integrates seamlessly with the HuggingFace ecosystem and employs lazy evaluation to minimize overhead. It effectively identifies high-uncertainty decision points and potential hallucinations, offering scalable, fine-grained insights for applications including model behavior diagnostics, uncertainty quantification, and production monitoring.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: (Large) Language Models
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Understanding and quantifying uncertainty in large language model (LLM) outputs is critical for reliable deployment. However, traditional evaluation approaches provide limited insight into model confidence at individual token positions during generation. To address this issue, we introduce LogitScope, a lightweight framework for analyzing LLM uncertainty through token-level information metrics computed from probability distributions. By measuring metrics such as entropy and varentropy at each generation step, LogitScope reveals patterns in model confidence, identifies potential hallucinations, and exposes decision points where models exhibit high uncertainty, all without requiring labeled data or semantic interpretation. We demonstrate LogitScope's utility across diverse applications including uncertainty quantification, model behavior analysis, and production monitoring. The framework is model-agnostic, computationally efficient through lazy evaluation, and compatible with any HuggingFace model, enabling both researchers and practitioners to inspect LLM behavior during inference.

Problem

• LLM uncertainty
• token-level confidence
• information metrics
• model hallucination
• uncertainty quantification

Innovation

• LogitScope
• LLM uncertainty
• token-level entropy
• varentropy
• model-agnostic framework