AINeutralarXiv – CS AI · 7h ago6/10
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Provenance-Grounded Gating and Adaptive Recovery in Synthetic Post-Training Data Curation
Researchers present a controlled study on synthetic data curation for post-training large language models, examining whether filtering decisions are grounded in source evidence and whether rejected samples can be recovered. Their findings show that provenance-aware filtering improves faithfulness detection, different gate types catch different errors, and adaptive recovery strategies significantly improve overall yield compared to simple resampling.