Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

LogitScope: A Framework for Analyzing LLM Uncertainty Through Information Metrics

Farhan Ahmed, Yuya Jeremy Ong, Chad DeLuca

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.

LogitScope: A Framework for Analyzing LLM Uncertainty Through Information Metrics

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.

Paper Structure

This paper contains 23 sections, 7 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Uncertainty Quantification in LLMs
  4. Model Monitoring and Drift Detection
  5. Method
  6. Information Metrics for Uncertainty Analysis
  7. Implementation
  8. Analysis and Applications
  9. Uncertainty Pattern Analysis
  10. Token-level patterns
  11. Aggregate patterns
  12. Applications
  13. Limitations and Future Work
  14. Conclusion
  15. Metric Definitions
  16. ...and 8 more sections

Figures (5)

  • Figure 1: Entropy vs varentropy scatter plot. Each point represents a token position, with lower-left indicating confident predictions and upper-right indicating multimodal uncertainty.
  • Figure 2: The web interface displaying the entropy metric. Tokens are color-coded by magnitude, with brighter colors indicating higher uncertainty. The sidebar shows aggregate statistics for quick assessment of overall model confidence.
  • Figure 3: The web interface displaying the varentropy metric. Varentropy reveals multimodal distributions where the model considers multiple alternatives. High varentropy regions indicate decision points with competing continuations.
  • Figure 4: The web interface displaying the perplexity metric. Lower values indicate better predictive confidence. The interface enables identification of regions where the model exhibits uncertainty.
  • Figure 5: The web interface displaying the probability metric. Higher values indicate more confident token selections. The direct probability measure provides intuitive assessment of model certainty.