Sequential statistical inference for Large Language Models: Representation, validity, and monitoring

2026-05-30

AI Summary

This work addresses the challenges of behavioral drift, miscalibrated uncertainty, and declining trustworthiness in large language models (LLMs) under continuous deployment, primarily caused by inadequate modeling and monitoring of temporal interactions and dynamic feedback. To tackle this, the paper introduces, for the first time, a sequential statistical inference framework tailored for trustworthy LLM deployment. Built upon dependent stochastic processes, the proposed framework integrates sequential hypothesis testing, change-point detection, calibration assessment, and fairness monitoring. This paradigm provides rigorous uncertainty guarantees in settings involving dependent interactions, repeated usage, and adaptive behavior, while enabling real-time monitoring and early warning for critical attributes such as hallucination rates, refusal patterns, and fairness violations. The approach significantly enhances the stability and reliability of LLMs operating in dynamic environments.

Abstract

This discussion argues that sequential statistical inference can naturally contribute to LLM trustworthiness. In deployment, LLM systems are queried repeatedly, conditioned on evolving contexts, and incorporate user or tool feedback, and may exhibit behavioral shifts after model updates or distribution changes. The discussion is organized around three tasks: representation, modeling LLM interactions as dependent stochastic processes rather than isolated prompt--response pairs; validity, developing uncertainty guarantees that remain meaningful under dependence, repeated use, and adaptation; and monitoring, using sequential alarms and change-point detection to identify shifts in calibration, hallucination rates, refusal behavior, fairness, or other task-relevant properties. This perspective complements recent surveys by viewing trustworthy LLM deployment as a problem of statistical process control.

Problem

• sequential inference
• large language models
• trustworthiness
• statistical process control
• behavioral shifts

Innovation

• sequential inference
• statistical process control
• uncertainty quantification
• change-point detection
• trustworthy LLMs