Fusion Cuisine for RAG: Z‑Scores, Rankers, and the Two‑Source Diet
Retrieval‑augmented generation tends to pick a side: either lean on labeled exemplars (ICL/L‑RAG) that encode task semantics, or on unlabeled corpora (U‑RAG) that provide broad knowledge. HF‑RAG argues we shouldn’t choose. Instead, it proposes a hierarchical fusion: (1) fuse multiple rankers within each source, then (2) fuse across sources by putting scores on a common scale. The result is a simple, training‑free recipe that improves fact verification and, crucially, generalizes better out‑of‑domain. ...