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    <abstract>Sample-level ranking is widely used for data selection, debugging, and training-data attribution, but redundant NLP corpora—exact and near duplicates, paraphrases, and templated examples—can make proxy-induced rankings unstable across random seeds. We study stable sample-level ranking under redundancy and propose SCARV, a modular aggregation layer that operates on top of existing scoring proxies. SCARV normalizes scores within each run, can regularize redundant examples through cluster-level aggregation and allocation, and aggregates across seeds with a robust operator. Across synthetic redundancy, naturally mined QQP redundancy, multiple proxy families, classification and sentence-pair tasks, and end-to-end DistilBERT fine-tuning, stability-aware aggregation improves over bare proxy rankings and yields more reproducible ranking-based decisions, such as selected subsets and suspicious-example sets. Matched mechanism analysis shows that cross-seed aggregation is the dominant generic stabilizer. The incremental effect of structural regularization is small, map-dependent, dataset-dependent, and sometimes negative; a meaningful redundancy map is useful mainly when group-consistent treatment is operationally desired. These results position SCARV as a stability-oriented aggregation framework rather than a universal data selector and emphasize decision reproducibility over uniform gains in downstream utility.</abstract>
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%0 Conference Proceedings
%T SCARV: Structure-Constrained Aggregation for Stable Sample Ranking in Redundant NLP Datasets
%A Wu, Feiyu
%A Zheng, Xu
%A Wang, Zhuocheng
%A Dai, YIMing
%A LI, Hui
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wu-etal-2026-scarv
%X Sample-level ranking is widely used for data selection, debugging, and training-data attribution, but redundant NLP corpora—exact and near duplicates, paraphrases, and templated examples—can make proxy-induced rankings unstable across random seeds. We study stable sample-level ranking under redundancy and propose SCARV, a modular aggregation layer that operates on top of existing scoring proxies. SCARV normalizes scores within each run, can regularize redundant examples through cluster-level aggregation and allocation, and aggregates across seeds with a robust operator. Across synthetic redundancy, naturally mined QQP redundancy, multiple proxy families, classification and sentence-pair tasks, and end-to-end DistilBERT fine-tuning, stability-aware aggregation improves over bare proxy rankings and yields more reproducible ranking-based decisions, such as selected subsets and suspicious-example sets. Matched mechanism analysis shows that cross-seed aggregation is the dominant generic stabilizer. The incremental effect of structural regularization is small, map-dependent, dataset-dependent, and sometimes negative; a meaningful redundancy map is useful mainly when group-consistent treatment is operationally desired. These results position SCARV as a stability-oriented aggregation framework rather than a universal data selector and emphasize decision reproducibility over uniform gains in downstream utility.
%U https://aclanthology.org/2026.findings-emnlp.525/
%P 11013-11035

Markdown (Informal)

[SCARV: Structure-Constrained Aggregation for Stable Sample Ranking in Redundant NLP Datasets](https://aclanthology.org/2026.findings-emnlp.525/) (Wu et al., Findings 2026)

• SCARV: Structure-Constrained Aggregation for Stable Sample Ranking in Redundant NLP Datasets (Wu et al., Findings 2026)

ACL

• Feiyu Wu, Xu Zheng, Zhuocheng Wang, YIMing Dai, and Hui LI. 2026. SCARV: Structure-Constrained Aggregation for Stable Sample Ranking in Redundant NLP Datasets. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 11013–11035, Budapest, Hungary. Association for Computational Linguistics.