TARG: Training-Free Adaptive Retrieval Gating for Efficient RAG

• URL: http://arxiv.org/abs/2511.09803v1
• Date: Fri, 14 Nov 2025 01:10:16 GMT
• Title: TARG: Training-Free Adaptive Retrieval Gating for Efficient RAG
• Abstract summary: Training-free Adaptive Retrieval Gating (TARG) is a single-shot policy that decides when to retrieve using only a short, no-latency draft from the base model.<n>On NQ-Open, TriviaQA, and PopQA, TARG consistently shifts the accuracy-efficiency frontier.
• Score: 46.122203287541005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-Augmented Generation (RAG) improves factuality but retrieving for every query often hurts quality while inflating tokens and latency. We propose Training-free Adaptive Retrieval Gating (TARG), a single-shot policy that decides when to retrieve using only a short, no-context draft from the base model. From the draft's prefix logits, TARG computes lightweight uncertainty scores: mean token entropy, a margin signal derived from the top-1/top-2 logit gap via a monotone link, or small-N variance across a handful of stochastic prefixes, and triggers retrieval only when the score exceeds a threshold. The gate is model agnostic, adds only tens to hundreds of draft tokens, and requires no additional training or auxiliary heads. On NQ-Open, TriviaQA, and PopQA, TARG consistently shifts the accuracy-efficiency frontier: compared with Always-RAG, TARG matches or improves EM/F1 while reducing retrieval by 70-90% and cutting end-to-end latency, and it remains close to Never-RAG in overhead. A central empirical finding is that under modern instruction-tuned LLMs the margin signal is a robust default (entropy compresses as backbones sharpen), with small-N variance offering a conservative, budget-first alternative. We provide ablations over gate type and prefix length and use a delta-latency view to make budget trade-offs explicit.

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