Datasets:

vigneshwar234
/

VORTEXRAG-Benchmarks

license: mit
task_categories:
  - question-answering
  - text-retrieval
language:
  - en
tags:
  - retrieval-augmented-generation
  - rag
  - causal-reasoning
  - multi-hop-qa
  - semantic-drift
  - context-window-poisoning
  - benchmark
  - evaluation
pretty_name: VORTEXRAG Benchmark Evaluation Data
size_categories:
  - 10K<n<100K

VORTEXRAG Benchmark Evaluation Dataset

Benchmark evaluation data, results, and per-layer ablation scores for the VORTEXRAG 7-layer causal RAG framework.

Description

This dataset contains:

Main benchmark results comparing VORTEXRAG against 7 baseline systems across NQ, HotpotQA, MuSiQue, and 2WikiMultiHopQA
Full ablation study showing per-layer contribution (configs A through H)
Per-dataset breakdown showing performance on 6 evaluation datasets
Domain preset evaluation for all 11 domain configurations
Hyperparameter sensitivity sweep data for τ and θ_CPG
Latency breakdown per layer on A100 GPU

Datasets Used for Evaluation

Dataset	Type	Questions	Hops	Source
NaturalQuestions	Open-domain QA	7,842	1–2	Wikipedia Dec 2018
HotpotQA	Multi-hop QA	7,405	2	Wikipedia 10K docs
MuSiQue	Multi-hop QA	2,417	2–4	Wikipedia filtered
2WikiMultiHopQA	Multi-hop QA	12,576	2	Wikipedia + Wikidata
LegalBench	Legal QA	1,200	1–3	US federal case law
MedQA	Medical QA	1,273	1–2	PubMed abstracts

Main Results

System	EM	F1	Faithfulness	SDR	CPR	Latency
Naive RAG	61.2	68.4	0.71	—	—	120ms
BM25+Rerank	59.8	66.1	0.69	—	—	95ms
HyDE	64.1	71.8	0.74	12%	8%	340ms
CRAG	66.9	74.3	0.78	31%	19%	290ms
Self-RAG	68.4	75.9	0.81	35%	24%	410ms
FiD	63.5	70.2	0.73	8%	5%	280ms
FLARE	65.7	72.9	0.75	14%	10%	320ms
VORTEXRAG	74.8	82.6	0.94	61%	71%	185ms

VORTEXRAG Benchmark Evaluation Dataset

Description

Datasets Used for Evaluation

Main Results

Links