🇮🇳 INDian Cultural commonsense Inventory with Cross-regional Answers (INDICA)
This is the repository for Common to Whom? Regional Cultural Commonsense and LLM Bias in India.
Authors: Sangmitra Madhusudan, Trush Shashank More, Steph Buongiorno, Renata Dividino, Jad Kabbara and Ali Emami
📄 Paper abstract
Existing cultural commonsense benchmarks treat nations as monolithic, assuming uniform practices within national boundaries. But does cultural commonsense hold uniformly within a nation, or does it vary at the sub-national level? We introduce INDICA, the first benchmark designed to test LLMs' ability to address this question, focusing on India—a nation of 28 states, 8 union territories, and 22 official languages. We collect human-annotated answers from five Indian regions (North, South, East, West, and Central) across 515 questions spanning 8 domains of everyday life, yielding 1,630 region-specific question-answer pairs. Strikingly, only 39.4% of questions elicit agreement across all five regions, demonstrating that cultural commonsense in India is predominantly regional, not national. We evaluate eight state-of-the-art LLMs and find two critical gaps: models achieve only 13.4%–20.9% accuracy on region-specific questions, and they exhibit geographic bias, over-selecting Central and North India as the "default" (selected 30-40% more often than expected) while under-representing East and West. Beyond India, our methodology provides a generalizable framework for evaluating cultural commonsense in any culturally heterogeneous nation, from question design grounded in anthropological taxonomy, to regional data collection, to bias measurement.
Dataset Structure
Files
dataset.json: Complete dataset (515 questions)
Data Format
{
"question_id": "q_196",
"question_text": "Are tinted windows allowed on vehicles, and what types of vehicles most commonly have them?",
"question_domain": "Traffic and transport behavior",
"question_subcategory": "Streets and traffic",
"question_topic": "Understanding Local Traffic Regulations",
"North": {
"answer": [
"In North India, tinted windows are not allowed on vehicles."
]
},
"South": {
"answer": [
"In South India, tinted windows are not allowed on vehicles."
]
},
"East": {
"answer": [
"In East India, tinted windows are not allowed on vehicles."
]
},
"West": {
"answer": [
"In West India, tinted windows are not allowed on vehicles."
]
},
"Central": {
"answer": [
"In Central India, tinted windows are not allowed on vehicles."
]
},
"pairwise_agreements": {
"South_North": {"agreement": true},
"South_East": {"agreement": true},
"South_West": {"agreement": true},
"South_Central": {"agreement": true},
"North_East": {"agreement": true},
"North_Central": {"agreement": true},
"East_Central": {"agreement": true},
"North_West": {"agreement": true},
"East_West": {"agreement": true},
"West_Central": {"agreement": true}
},
"universal_agreement": true
}
Domains (8)
- Interpersonal Relations
- Festivals and Rituals
- Traffic and transport behavior
- Education
- Clothing and adornment
- Food processing and consumption
- Communication
- Finance
Usage
from datasets import load_dataset
import json
# Load via Hugging Face
dataset = load_dataset("Sangmitra-06/INDICA", data_files="dataset.json")
# Or load directly
with open('full_dataset.json') as f:
data = json.load(f)
Citation
@misc{madhusudan2026commonwhomregionalcultural,
title={Common to Whom? Regional Cultural Commonsense and LLM Bias in India},
author={Sangmitra Madhusudan and Trush Shashank More and Steph Buongiorno and Renata Dividino and Jad Kabbara and Ali Emami},
year={2026},
eprint={2601.15550},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2601.15550},
}