arXiv:2601.21996v2 Announce Type: replace-cross Abstract: While Mechanistic Interpretability has identified interpretable circuits in LLMs, their causal origins in training data remain elusive. We introduce Mechanistic Data Attribution (MDA), a scalable framework that employs Influence Functions to trace interpretable units back to specific training samples. Through extensive experiments on the Pythia family, we causally validate that targeted intervention--removing or augmenting a small fraction of high-influence samples--significantly modulates the emergence of interpretable heads, whereas r
The accelerating pace of large language model (LLM) development and deployment necessitates deeper understanding of their internal mechanisms for safety, auditing, and performance optimization.
This research provides a critical tool for understanding how specific training data shapes interpretable units within LLMs, moving beyond black-box approaches to enable more controlled and explainable AI systems.
The ability to trace LLM unit origins to training data transforms interpretability from observation to causal intervention, paving the way for more targeted model improvement and bias mitigation.
- · AI safety researchers
- · LLM developers
- · Auditors and regulators
- · Ethical AI advocates
- · Developers relying solely on black-box optimization
- · Companies with opaque data pipelines
Enhanced ability to debug, audit, and improve LLMs by understanding the causal link between training data and internal model units.
Development of tools that automatically flag or adjust training data based on its influence on problematic or desired model behaviors.
New regulations requiring data traceability and explainability for critical AI deployments, impacting data collection and model training practices across industries.
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Read at arXiv cs.LG