Quantifying Prior Dominance in RAG Systems
Speaker: Dr. Barak Or, Founder - ArtificialGate
"Retrieval-Augmented Generation (RAG) grounds Large Language Models in external knowledge, yet current evaluations rely on discrete heuristics that suffer from 'epistemic blindness' - failing to distinguish genuine contextual information extraction from parametric memory recall."
The Research Context
Standard benchmarks like Exact Match (EM) and F1 scores provide a binary view of performance. This research shifts the focus toward continuous probability spaces, introducing the Normalized Context Utilization (NCU) metric to isolate the generator's mechanical efficacy from its pre-existing weights.
Figure 1: Transitioning from discrete text matching to continuous log-probabilities to isolate informational gain.
Core Empirical Findings
The SLM Advantage
Small Language Models (1.5B - 7B) demonstrate statistical parity with 72B parameter models in contextual adherence, while offering up to a 70x reduction in inference latency.
Prior Dominance
Massive commercial APIs frequently overrode explicit external evidence (47.1% rejection rate) in favor of internal parametric priors, despite strict extraction instructions.
| Metric / Behavior | SLMs (1.5B - 7B) | Massive Commercial APIs |
|---|---|---|
| Contextual Adherence | High (Statistical Parity) | High (Baseline) |
| Prior Dominance (Evidence Rejection) | Low | 47.1% Overridden |
| Inference Latency | Up to 70x Faster | Baseline |
Figure 2: Visualizing the collapse of predictive confidence when parametric priors are contradicted by external evidence.
The "Alignment Tax" Hypothesis
Why do high-capacity models fail at simple extraction? The session will explore how safety alignments (RLHF) and dense parametric memorization can create systemic resistance to out-of-distribution updates, leading to "Negative Transfer" and confidence collapse.
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