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…