Beyond Surface-Level Similarity: Hierarchical Contamination Detection for Synthetic Training Data in Foundation Models

Documenti analoghi

• When Are Learning Biases Equivalent? A Unifying Framework for Fairness, Robustness, and Distribution Shift
  di: Mehta, Sushant
  Pubblicazione: (2025)
• Scaling Laws and In-Context Learning: A Unified Theoretical Framework
  di: Mehta, Sushant, et al.
  Pubblicazione: (2025)
• Anomaly Detection with Adaptive and Aggressive Rejection for Contaminated Training Data
  di: Lee, Jungi, et al.
  Pubblicazione: (2025)
• TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models
  di: Li, Hongkai, et al.
  Pubblicazione: (2026)
• EnterpriseBench Corecraft: Training Generalizable Agents on High-Fidelity RL Environments
  di: Mehta, Sushant, et al.
  Pubblicazione: (2026)