Rare Event Analysis of Large Language Models

2026-02-06

AI Summary

This work addresses the challenge posed by rare yet high-impact behaviors exhibited by large language models during inference—events that are difficult to detect during development but introduce unpredictable risks at scale. The paper presents the first end-to-end, systematic analysis framework that integrates probabilistic modeling, efficient sampling, probability estimation, and error analysis to enable controlled detection and quantitative assessment of such low-probability, high-consequence behaviors. Designed to be generalizable across models and deployment scenarios, the framework successfully identifies and quantifies multiple previously unseen rare behaviors absent from both training and standard evaluation datasets, thereby advancing the study of rare events from theoretical inquiry to actionable engineering practice.

Technology Category

Machine Learning: Calibration & Uncertainty QuantificationReasoning under Uncertainty: Relational Probabilistic ModelsNatural Language Processing: (Large) Language Models

Application Category

Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsUser Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systemsWeb Mining and Content Analysis: Large pretrained models with web data

Abstract

Being probabilistic models, during inference large language models (LLMs) display rare events: behaviour that is far from typical but highly significant. By definition all rare events are hard to see, but the enormous scale of LLM usage means that events completely unobserved during development are likely to become prominent in deployment. Here we present an end-to-end framework for the systematic analysis of rare events in LLMs. We provide a practical implementation spanning theory, efficient generation strategies, probability estimation and error analysis, which we illustrate with concrete examples. We outline extensions and applications to other models and contexts, highlighting the generality of the concepts and techniques presented here.

Problem

• Rare Event
• Large Language Models
• Inference
• Probabilistic Models
• Deployment

Innovation

• rare event analysis
• large language models
• probabilistic inference
• systematic framework
• probability estimation