A Distributional Framework for Behavioral Characterization and Risk Management of Large Language Models

Abstract:Large language models are stochastic by nature. Repeated executions under the same experimental conditions may produce different responses. We propose a framework that can be applied across the model lifecycle, from task design to residual behavior and continuous monitoring. The method consists of building an empirical response distribution estimated through Monte Carlo simulation. Semantic interventions are introduced to study how these distributions change. We illustrate the framework with two different tasks, borrower classification and music album recommendation, and quantify the resulting distributional changes. The framework is linked to model risk management and governance by measuring robustness and control effectiveness, and characterizing residual behavior. Since it relies only on observed responses, it can also be applied to black-box models accessed through commercial APIs.

Risk Management (q-fin.RM); Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2610.12718 [q-fin.RM]

(or arXiv:2610.12718v1 [q-fin.RM] for this version)

https://doi.org/10.48550/arXiv.2610.12718 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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