How RAG Systems Could Transform NLP Evaluation

Let me say this plainly: The way we evaluate NLP systems is about to change. Retrieval-augmented generation (RAG) systems are spotlighting a critical gap in NLP evaluation by introducing a factor we can no longer ignore, source dependence.

A New Dimension of Evaluation

Traditionally, when an NLP system answers a question, we're satisfied if it spits out a single, seemingly correct answer. But what happens when different sources provide different answers? Enter RAG systems, which reveal how much we miss by sticking to one source.

So why does this matter? In fields like transplant patient education, institutional sources often disagree. RAG systems like TransplantQA, a benchmark featuring real patient questions, expose these discrepancies by grounding answers in multiple institutional handbooks.

The Unseen Disagreements

HERO-QA takes it further. It's a hierarchical retrieval strategy that not only finds answers but audits them too. The twist? A structured-output judge scores how these sources relate using a validated 5-label taxonomy. The result: more disagreement than we ever assumed. It's not just about intensity. It's about prevalence.

The asymmetry is staggering. If better retrieval can reveal hidden disagreements, what else are we overlooking? The best investors in the world are adding this capability to their NLP toolkits.

Beyond Just NLP

Think this is just an issue for healthcare? Think again. This framework isn't confined to one domain. It's equally applicable in legal and educational contexts. As NLP systems evolve, measuring source-dependence isn't just a nice-to-have. It's a responsibility.

Everyone is panicking. Good. It's the push we need to rethink how we evaluate NLP systems. The adoption curve for RAG systems is just starting to climb. Long AI Models, long patience.