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LectureBank-Full Prerequisite QA Benchmark
LectureBank-Full is the expanded configuration of the LectureBank corpus used as a prerequisite-QA / retrieval benchmark. It contains English NLP-related lecture slide files and manual concept-pair prerequisite annotations over topics. The task is to retrieve, for a given target concept (query), the set of gold prerequisite concepts from a fixed candidate pool. Systems are scored with ranking metrics such as Recall@ (R@); R@ measures the fraction of gold prerequisites recovered in the top- retrieved candidates. Standard splits hold out concept pairs for evaluation, and headline comparisons fix the encoder, candidate pool, cutoff, matching rule, and split policy.
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LectureBank-Full R@10 Gain from Diffusion and Role-Aware Quotas
LectureBank-Full Configuration Used in Hierarchical Prerequisite RAG (208 Concepts, 899 Edges, 1,421 QA)
LectureBank-Full Target-Disjoint R@10 Result (n=164): Diffusion Gain Survives, Adaptive Tied
LectureBank-Full Generation Diagnostic: Token-F1 1.9 → 18.3, EM Stays 0.0
LectureBank-Full Error Taxonomy: Residual Misses Are Near-Misses Along the Local Prerequisite Graph
LectureBank-Full Paired Delta: Adaptive vs Hierarchical Baseline = +0.7 [-2.1, +3.6]
Token-Cap Comparison on LectureBank-Full: Adaptive Loses More as Cap Tightens
LectureBank-Full Tight-Budget Advantage of Adaptive Depth Gating (Mean ΔR@k = +2.13 over k∈{1,2,3,4})
LectureBank-Full ΔR@k Peaks at k=4 (+6.4 Points, CI [1.0, 11.7])
LectureBank-Full Diffusion Gain over Static Parent Expansion (~18 R@10 Points)
Bounded Held-Out Targets After Strictest Leakage Control (21 LectureBank-Full, 18 MOOC-CS)
LectureBank-Full Decomposition: Diffusion+Quotas Drive ~18 R@10 Points; Contrast Gating Adds At Most ~1 Point (Statistically Tied)