When Your LLM Reaches End-of-Life: A Framework for Confident Model Migration in Production Systems
The paper presents a framework for migrating production Large Language Models (LLMs) when they reach end-of-life or require replacement. It introduces a Bayesian statistical method that aligns automated evaluation metrics with human judgments, enabling reliable model comparisons with limited manual data. The approach was tested on a commercial question-answering system handling 5.3 million monthly interactions across six regions, assessing correctness, refusal behavior, and stylistic consistency.
- ▪The framework uses a Bayesian statistical approach to calibrate automated evaluation metrics with human judgments.
- ▪It was applied to a commercial question-answering system handling 5.3 million monthly user interactions across six global regions.
- ▪The method evaluates replacement models on correctness, refusal behavior, and stylistic adherence to ensure quality.
- ▪The framework supports reproducible and efficient model migration for enterprises using multiple LLMs across diverse use cases and regions.
- ▪This approach addresses the growing need for systematic model replacement as the LLM ecosystem rapidly evolves.
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Computer Science > Artificial Intelligence arXiv:2604.27082 (cs) [Submitted on 29 Apr 2026] Title:When Your LLM Reaches End-of-Life: A Framework for Confident Model Migration in Production Systems Authors:Emma Casey, David Roberts, David Sim, Ian Beaver View a PDF of the paper titled When Your LLM Reaches End-of-Life: A Framework for Confident Model Migration in Production Systems, by Emma Casey and 3 other authors View PDF HTML (experimental) Abstract:We present a framework for migrating production Large Language Model (LLM) based systems when the underlying model reaches end-of-life or requires replacement.
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