LogitScope: A Framework for Analyzing LLM Uncertainty Through Information Metrics

TL;DR

LogitScope is a lightweight, model-agnostic framework that analyzes large language model uncertainty at the token level using information metrics, aiding in understanding, diagnosing, and monitoring model confidence without needing labeled data.

Contribution

We introduce LogitScope, a novel framework that computes token-level information metrics to analyze LLM uncertainty, revealing confidence patterns and potential hallucinations during inference.

Findings

Effectively identifies high-uncertainty points in LLM outputs

Detects hallucinations and confidence patterns without labeled data

Compatible with any HuggingFace model and computationally efficient

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.