First-Order In-Context Shapley: Scalable Data Auditing for LLM Alignment and Evaluation

arXiv:2607.22766v2 Announce Type: replace-cross Abstract: Post-training alignment of Large Language Models is increasingly bottlenecked by data quality: preference and instruction-tuning corpora accumulate label noise, safety inversions, and inter-annotator contradictions that deduplication misses and full-corpus LLM judging cannot afford. While Data Shapley offers a principled valuation framework, exact computation requires intractable retraining, and embedding surrogates like KNN-Shapley fail to separate high- from low-utility demonstrations ($\rho = 0.0003$ with generative utility). We intr

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