arXiv:2605.30348v1 Announce Type: cross Abstract: The pretraining data mixture of Large Language Models (LLMs) constitutes their "digital DNA", shaping model behaviors, capabilities, and failure modes. Yet this composition is rarely disclosed, making post-hoc auditing of data combination or provenance difficult. In this work, we formalize $\textbf{{Data Mixture Surgery (DMS)}}$: given only generated text from a target LLM, estimate the domain-level distribution of its pretraining corpus under a predefined taxonomy. We propose $\textbf{{LLMSurgeon}}$, a strong framework that casts DMS as an inv
As LLMs become increasingly central to AI development and deployment, the need for transparency and audibility of their foundational training data is growing due to regulatory pressure and ethical concerns.
This research provides a critical tool for understanding the underlying biases and capabilities of LLMs, enabling better governance, fair use, and competitive analysis in the AI ecosystem.
The ability to reverse-engineer an LLM's data mixture allows for post-hoc auditing and comparison without requiring access to proprietary training details, shifting power dynamics towards greater transparency and accountability.
- · AI auditors
- · Regulatory bodies
- · Ethical AI researchers
- · Enterprises evaluating third-party LLMs
- · LLM developers withholding data mixture details
- · Black-box AI systems
- · Proprietary model developers relying on secrecy
Regulators will gain a powerful new mechanism to enforce data provenance and fairness in LLMs, influencing market access and product development.
Increased transparency regarding data mixtures could lead to a 'race to purity' or standardization in training datasets as LLM providers seek to demonstrate ethical and unbiased foundations.
This capability could foster a more open ecosystem for AI development, potentially reducing the dominance of a few large players by leveling the playing field for auditing and understanding model behavior.
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Read at arXiv cs.LG