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/Symbiosis Skills and Professional University

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ICL-Prompt-Sensitivity-Risk-Dataset

Simulated few-shot accuracy variance for 2800 in-context-learning configurations across five India-relevant tasks, four example-selection methods, prompt-order optimization, calibration, and formatting consistency, grounded in the foundational and 2021-2023 ICL robustness literature. Forward-looking task predicts prompt fragility from just a few cheap evaluation probes.

4.50

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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 how few-shot accuracy swings by tens of points purely from example order, selection, and formatting choices. 2800 configs across five tasks (three India-relevant: vernacular sentiment, agri-query intent, grievance routing), four selection methods, order/calibration/format flags, shot count. Ten sources (2020–2024, weighted 2021–2023), including GPT-3, Calibrate Before Use, Fantastically Ordered Prompts, Rethinking the Role of Demonstrations, and the prompt-format-sensitivity paper.

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 12:44:40

Visibility

Open

Hosted / Redirected

Hosted

Tags

Large Language Model

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