How much of an LLM-generated clinical corpus is actually new? A production-scale measurement of content redundancy for provenance classification
arXiv:2606.29605v1 Announce Type: new Abstract: Clinical machine learning increasingly relies on training corpora generated by large language models (LLMs) rather than annotated by clinicians, and such corpora are described and reused largely on the basis of their reported scale. We test whether volume reflects information content. Analysing the complete output of a multi-agent clinical extraction pipeline applied to 167,034 patient narratives, 2.51 billion generated tokens across the ten text-bearing channels of an eleven-channel pipeline, we introduce Provenance-based Redundancy Decompositio
The proliferation of LLM-generated data for training clinical AI models necessitates urgent investigation into the quality and redundancy of these synthetic corpora, especially as deployment scale increases.
This research directly challenges the assumption that larger LLM-generated datasets inherently contain more novel information, potentially impacting the efficiency and efficacy of AI model development in critical applications like healthcare.
The methodology for evaluating LLM-generated clinical datasets will likely shift from sheer volume to metrics of uniqueness and provenance, influencing data generation strategies and model training pipelines.
- · AI researchers focusing on data efficiency
- · Clinical institutions adopting AI with robust data provenance standards
- · Developers of tools for content redundancy analysis
- · LLM providers prioritizing volume over distinctiveness
- · AI projects relying solely on large-scale synthetic data without validation
- · Clinical AI models trained on highly redundant data
Demand will increase for methods and tools to measure and optimize the information content, rather than just the scale, of LLM-generated training data.
This shift could lead to more robust and less 'hallucinatory' clinical AI models, but also potentially slower development cycles if data generation becomes more complex and scrutinized.
Ethical and regulatory bodies may begin to impose stricter guidelines on the provenance and uniqueness of data used for training sensitive AI systems, especially in healthcare.
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Read at arXiv cs.CL