arXiv:2606.04928v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed across diverse applications, raising critical questions for governance, accountability, and data provenance. Understanding which training data most influenced a model's output remains a fundamental open problem. We address this challenge through training data attribution (TDA) for auto-regressive LLMs by expanding upon the inverse formulation: How would training data be affected if the model had seen the generated output during training? Our method perturbs the base model using bidirectional
The increasing deployment of LLMs across diverse applications highlights the urgent need for robust governance and accountability frameworks, making data attribution a critical and timely problem.
Understanding data provenance in LLMs is crucial for regulatory compliance, mitigating bias, intellectual property rights, and building trust in AI systems.
This method provides a novel approach to trace LLM outputs back to specific training data, enhancing transparency and accountability in AI development and deployment.
- · AI Governance bodies
- · Data owners
- · Auditors
- · Responsible AI developers
- · Developers with opaque models
- · Bad actors exploiting data
- · Users impacted by biased AI without recourse
Improved ability to identify and address issues related to data bias and intellectual property infringement in LLMs.
Increased pressure on AI developers to maintain high standards of data documentation and provenance throughout the model lifecycle.
Potential for new regulatory frameworks specifically mandating data attribution capabilities for critical AI deployments.
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Read at arXiv cs.LG