Rare Event Analysis of Large Language Models

TL;DR

This paper introduces a comprehensive framework for analyzing rare but significant events in large language models, addressing challenges in detection, estimation, and understanding of these uncommon behaviors during deployment.

Contribution

It presents a novel end-to-end methodology combining theory, generation, probability estimation, and error analysis for systematic rare event analysis in LLMs.

Findings

Framework effectively identifies rare events in LLMs.

Method improves understanding of rare behaviors during deployment.

Applicable to various models and contexts.

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.