Research paper highlights specification mismatch as key to data attribution discrepancies

A new research paper titled "Which Influence Are We Estimating? The Role of Counterfactual Specifications in Data Attribution" explores the fundamental reasons behind discrepancies in influence estimators used for data debugging and valuation. The authors argue that differing rankings from these estimators stem not just from approximation errors, but more significantly from mismatches in how the behavior, interventions, and counterfactual training processes are specified. They formalize influence as a counterfactual estimand and categorize existing estimators by their implied specifications. Experiments demonstrate that these specification choices, particularly for surrogate behaviors like query loss, can substantially alter attribution quality and effectively identify target-specific training examples. AI

IMPACT Clarifies fundamental issues in data attribution, potentially leading to more reliable AI debugging and model valuation tools.

RANK_REASON Academic paper published on arXiv detailing a new theoretical framework for understanding data attribution in AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Counterfactual Specifications
• Data attribution using frequent pattern analysis
• Hugging Face
• Influence estimators
• Which Influence Are We Estimating? The Role of Counterfactual Specifications in Data Attribution

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →