arXiv:2606.05165v1 Announce Type: new Abstract: Training Data Attribution (TDA) seeks to trace a model's predictions back to its training data. The gold standard for TDA relies on causal interventions, observing how a model changes when data is added or removed, but repeated retraining is computationally challenging for Large Language Models (LLMs). Consequently, most approaches approximate this effect in the parameter space using gradients. However, tracking gradients across billions of parameters is not only prohibitively expensive but relies on local approximations. In this work, we propose
The increasing complexity and opacity of LLMs necessitate improved methods for data governance, intellectual property, and regulatory compliance. Current data attribution methods are computationally prohibitive, driving innovation in more efficient alternatives.
Accurate and efficient training data attribution is critical for auditability, intellectual property rights enforcement, and debugging in large AI models, addressing key challenges in responsible AI development and deployment.
The proposed 'STRIDE' method offers a more scalable approach to understanding which training data points influence specific model predictions, moving beyond expensive retraining or local gradient approximations.
- · AI developers and researchers
- · AI audit and compliance firms
- · Data providers
- · Developers relying on opaque or unsourced data
- · Existing computationally expensive attribution methods
Increased ability to trace the lineage and influence of training data within large language models.
Improved compliance with data privacy regulations and stronger intellectual property protections for data used in AI training.
The development of 'explainable AI debt' where models without clear data attribution face regulatory or market disadvantages.
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Read at arXiv cs.LG