  This position paper proposes a data-centric viewpoint of AI research,
focusing on large language models (LLMs). We start by making the key
observation that data is instrumental in the developmental (e.g., pretraining
and fine-tuning) and inferential stages (e.g., in-context learning) of LLMs,
and yet it receives disproportionally low attention from the research
community. We identify four specific scenarios centered around data, covering
data-centric benchmarks and data curation, data attribution, knowledge
transfer, and inference contextualization. In each scenario, we underscore the
importance of data, highlight promising research directions, and articulate the
potential impacts on the research community and, where applicable, the society
as a whole. For instance, we advocate for a suite of data-centric benchmarks
tailored to the scale and complexity of data for LLMs. These benchmarks can be
used to develop new data curation methods and document research efforts and
results, which can help promote openness and transparency in AI and LLM
research.
