Your retriever’s recall@5 improved four points after fi...

A strong answer identifies that the retriever and the generator are being optimized for different goals. The retriever is tuned to match human relevance labels, but the end-to-end pipeline relies on the generator’s log-likelihood of the output given the retrieved document and input. Since there is no mechanism connecting these two objectives, the retriever’s improvement does not guarantee downstream accuracy. To diagnose this, one should score the top-k retrieved documents through the generator to build a QLM (Query-Likelihood Model) and measure the divergence against the PR (Passage Relevance) labels to quantify the gap.