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RAG-Faithfulness-Hallucination-Risk-Dataset

Description Simulated hallucination rate across retrieved-passage-count (k) and RAG method for 600 configurations spanning five tasks and five methods (naive concatenation, FiD, REPLUG, Self-RAG, robust-trained RAG), grounded in the foundational and 2023 retrieval-augmented-generation literature. Forward-looking task predicts faithfulness at deployment-scale retrieval (k=20) from a single cheap top-passage probe (k=1).

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updated 12 Aug 2026

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COVERAGE India

LICENSE Attribution 4.0 International (CC BY- 4.0)

FORMAT Synthetic

TYPE Structured

SECTOR Science, Technology and Research

UPDATES NA

About Dataset

Simulates hallucination risk in RAG as a function of retrieval depth and method robustness to noisy passages. 600 configs across five tasks (two India-relevant: vernacular-language QA, agricultural advisory QA), k swept 1–20. Ten sources (2020–2024) in rag_theory.py, including RAG, FiD, REPLUG, Self-RAG, and the RGB benchmark.

Dataset Metadata

License

Attribution 4.0 International (CC BY- 4.0)

Sector

Science, Technology and Research

Author

Jai Mali

Source Organisation

Symbiosis Skills and Professional University

Uploaded By

Data Quality Score (Beta)

4.5

Dataset Type

Structured

Data Type

Synthetic

Task Category

N.A.

Time Granularity

NA

Frequency

NA

Geographic Granularity

N.A.

Primary Key / Indicator

N.A.

Data Collection Method

Manually curated

Language

N.A.

Year Range

N.A.

Date & Time

08/08/26 12:41:43

Visibility

Open

Hosted / Redirected

Hosted

Tags

Large Language Model

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