Observability in Natural Language Processing (NLP) Systems

Observability is a key concept in modern software engineering that allows engineers and operators to understand the health of a system by observing its external outputs. Upon discovery of issues, corrective action can be taken to ensure that a system performs as expected. Instead of hunting for issues, teams can focus on fixing them. With the recent rise of generative Natural Language Processing (NLP) systems, the need for observability in such systems has become apparent. While observability for classical software systems is increasingly popular, hardly any research exists on the same concept for generative NLP systems. This report calls for more research in the field, and explores how to transfer selected key concepts of observability to the field of generative NLP systems, discussing some common challenges and proposing a refined definition of observability for generative NLP systems. Challenges in generative text models like data drift, concept drift and adherence to topic and goals are discussed and evaluated. Speculative approaches to address these challenges are outlined.