🤖 AI Summary
为解决RAG检索不足问题，通过比较基于分数、内容和混合三种QPP方法，发现24个非词汇特征在效率和准确性上表现最优。
📝 Abstract
Standard Retrieval-Augmented Generation (RAG) pipelines often provide no reliable inference-time signal of whether retrieval succeeded; on ambiguous or out-of-scope queries, generation may then hallucinate. Motivated by a Czech nuclear-regulator deployment where data sensitivity precludes third-party LLM APIs, we compare three Query Performance Prediction (QPP) paradigms for retrieval sufficiency in RAG: score-based features, a content-based LLM judge, and a hybrid. On the eight ViDoRe vision domains (14,514 queries), our 24 non-lexical features (GeneralQPP; 15 distribution-shape, 5 query-surface, 4 global) reach a weighted-average AUROC of 0.856 at 2 ms per query, ahead of a classic-QPP literature pool (Classic Full, 0.835) and well above a local Qwen3.5 LLM judge (0.649, +0.207 gap; $\sim$3000$\times$ faster and cheaper per query). Adding the LLM judgment as one feature (hybrid) matches S1 on ViDoRe (0.863) but gains a statistically significant edge on SÚJB (AUROC 0.911 at Hit@5, adversarial-detection 0.954; 1,510 queries, 500 synthetic adversarial), at LLM latency. Rankings agree across datasets (Spearman $ρ= 0.90$). Under Leave-One-Domain-Out, S1 drops to 0.706; a 13-feature LODO-stepwise subset (S1-Lean) recovers to 0.719 (+0.032 over the literature pool).