Data-Centric AI in the Age of Large Language Models

TL;DR

This paper emphasizes the critical role of data in the development and use of large language models, advocating for a data-centric approach with new benchmarks and research directions to enhance transparency and effectiveness.

Contribution

It introduces a data-centric perspective for LLMs, proposing benchmarks and research directions to improve data curation, attribution, transfer, and contextualization.

Findings

Data is crucial in LLM development and inference stages.

Proposes data-centric benchmarks for LLM research.

Highlights potential societal impacts of data-focused AI.

Abstract

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.