What Data Shapes LLM Performance? Why This Paper Proposes Data Probes
What Data Shapes LLM Performance? Why This Paper Proposes Data Probes The paper "Position: Let's Develop Data Probes to Fundamentally Understand How Data Affects LLM Performance" argues that data is central to large language models, but that we still do not understand well what kinds of data help at different stages of the LLM workflow, including training, tuning, alignment, and in-context learning. Released on arXiv in May 2026, the paper frames this as a basic research gap: current practice often finds useful data through repeated large-scale experiments, but that does not necessarily explain why the data works. [S1] [S1] Paper overview: what was proposed, and why This paper is a position paper rather than a claim that the problem is already solved. Its main proposal is to develop "data probes" as a way to more fundamentally study how data affects LLM performance. The problem setting is broad: the authors point out that data matters across multiple stages o...