Evaluating the Impact of Automated Labeling on Retrieval Instability in Neural IR

Effective information retrieval (IR) depends on accurate relevance classification. But when the criteria are subjective or underspecified, small variations in classification can cause consequential shifts in retrieval results. The potential for such variability becomes critical for institutions when they use IR for research assessment. Retrieval instability can lead to relevant literature being overlooked, hindering a comprehensive understanding of the research landscape, and potentially undermining the validity of subsequent analyses and decisions.