Uncertainty Propagation in LLM-Based Systems: A Systems-Level Analysis

A recent submission to arXiv in the Computer Science > Software Engineering category presents a comprehensive framework aimed at elucidating the propagation of uncertainty within complex systems that utilize large language models (LLMs). The authors contend that uncertainty extends beyond individual model outputs, influencing internal model dynamics, various workflow phases, component interfaces, enduring states, and human or organizational interactions. If not addressed systematically, initial inaccuracies can spread and intensify, making them hard to identify and manage. The paper outlines a conceptual framework for identifying propagated uncertainty signals, proposes a structured taxonomy that includes intra-model (P1), system-level (P2), and socio-technical (P3) propagation methods, and highlights five significant research challenges. This work seeks to lay the groundwork for the development of more resilient and manageable LLM-based systems.