SCARV: Structure-Constrained Aggregation for Stable Sample Ranking in Redundant NLP Datasets
Sample-level rankings are increasingly used in data-centric NLP for analysis, filtering, debugging, and curation, yet existing pipelines typically score training examples pointwise and rank them as if they were independent. This assumption is fragile in the presence of exact duplicates, near-duplicates, paraphrases, and other redundant structure common in NLP corpora, where stochastic training can make highly similar examples receive unstable relative orderings across random seeds. We study stable sample-level ranking under redundancy and propose \textsc{SCARV}, a modular aggregation framework that operates on top of an existing scoring proxy. \textsc{SCARV} combines robust multi-seed agg...
| Authors | Xu Zheng, Feiyu Wu, Linhong Wu et al. |
|---|---|
| Primary category | cs.IR |
| Published | 1 May 2026 |
| Last updated at source | 1 May 2026 |
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