Problem
Research questions and friction points this paper is trying to address.
Large Language Model
Model Migration
End-of-Life
Production Systems
Evaluation Metrics
When Your LLM Reaches End-of-Life: A Framework for Confident Model Migration in Production Systems
🤖 AI Summary
This work addresses the lack of efficient and reliable methods for transferring large language models (LLMs) into production systems by proposing a Bayesian statistical framework for transfer evaluation. The approach leverages Bayesian calibration to align automated evaluation metrics with limited human annotations, significantly improving assessment efficiency while maintaining high quality. It enables reproducible transfer decisions across multiple regions and deployment scenarios by innovatively integrating calibrated automated metrics with human judgment. The framework effectively evaluates substitute models along critical dimensions—including factual correctness, refusal behavior, and stylistic consistency—and has been successfully deployed in a commercial question-answering system handling 5.3 million monthly interactions, where it accurately identified suitable model replacements.
📝 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. The key contribution is a Bayesian statistical approach that calibrates automated evaluation metrics against human judgments, enabling confident model comparison even with limited manual evaluation data. We demonstrate this framework on a commercial question-answering system serving 5.3M monthly interactions across six global regions; evaluating correctness, refusal behavior, and stylistic adherence to successfully identify suitable replacement models. The framework is broadly applicable to any enterprise deploying LLM-based products, providing a principled, reproducible methodology for model migration that balances quality assurance with evaluation efficiency. This is a capability increasingly essential as the LLM ecosystem continues to evolve rapidly and organizations manage portfolios of AI-powered services across multiple models, regions, and use cases.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bayesian calibration
LLM migration
human-AI evaluation alignment
production LLM systems
model replacement
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