# ๐Ÿ‡ฎ๐Ÿ‡ณ 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](https://arxiv.org/abs/2601.15550). **[Github Repository](https://github.com/Sangmitra-06/INDICA/)** Authors: Sangmitra Madhusudan, Trush Shashank More, Steph Buongiorno, Renata Dividino, Jad Kabbara and Ali Emami ## ๐Ÿ“„ 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 Indiaโ€”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%โ€“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 ```json { "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 ```python 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 ```bibtex @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}, } ```