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Beyond Shapley: An Influence-Based Data Auditing Pipeline for LLM Alignment and Evaluation
arXiv cs.LG — Machine Learning
Factual evidence
What the source reports
Researchers proposed an influence-based data auditing pipeline to identify safety risks and annotation errors in LLM fine-tuning datasets.
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- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.LG — Machine Learning · RESEARCH
- Published by source
- 12 October 2026
- Collected by OneBench
- 28 Jul 2026, 15:18 UK
Stored source excerpt
arXiv:2607.22766v1 Announce Type: new Abstract: The alignment of Large Language Models (LLMs) is increasingly bottlenecked by data quality. As datasets scale, massive preference and instruction-tuning…
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