What strategies can be used for longitudinal evaluation of LLMs?

Longitudinal evaluation of LLMs can involve continuous testing over time to assess model performance stability and drift. Strategies include implementing regular sampling of outputs across various inputs and checking for consistency in metrics like accuracy and relevance. It can also involve time-series analysis of performance metrics, comparing results from different evaluation phases, and incorporating feedback loops where user interactions influence ongoing evaluations. Additionally, automated testing frameworks can facilitate regular updates of model performance against established benchmarks.