  Generative models are increasingly being used in various applications, such
as text generation, commonsense reasoning, and question-answering. To be
effective globally, these models must be aware of and account for local
socio-cultural contexts, making it necessary to have benchmarks to evaluate the
models for their cultural familiarity. Since the training data for LLMs is
web-based and the Web is limited in its representation of information, it does
not capture knowledge present within communities that are not on the Web. Thus,
these models exacerbate the inequities, semantic misalignment, and stereotypes
from the Web. There has been a growing call for community-centered
participatory research methods in NLP. In this work, we respond to this call by
using participatory research methods to introduce $\textit{DOSA}$, the first
community-generated $\textbf{D}$ataset $\textbf{o}$f 615 $\textbf{S}$ocial
$\textbf{A}$rtifacts, by engaging with 260 participants from 19 different
Indian geographic subcultures. We use a gamified framework that relies on
collective sensemaking to collect the names and descriptions of these artifacts
such that the descriptions semantically align with the shared sensibilities of
the individuals from those cultures. Next, we benchmark four popular LLMs and
find that they show significant variation across regional sub-cultures in their
ability to infer the artifacts.
