Beyond Shapley: An Influence-Based Data Auditing Pipeline for LLM Alignment and Evaluation
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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 corpora inevitably accumulate hidden structural contradictions, safety risks, and systemic human annotation errors. Standard dataset auditing methods, such as semantic deduplication or LLM-as-a-judge, struggle to capture the actual predictive impact of individual records and often miss deep…
1Key Takeaways
- 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 corpora inevitably accumulate hidden structural contradictions, safety risks, and systemic human annotation errors.
- Standard dataset auditing methods, such as semantic deduplication or LLM-as-a-judge, struggle to capture the actual predictive impact of individual records and often miss deep….
2AIWedia Score
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3Why it matters
Research breakthroughs often arrive in products months later—early signals matter for strategy. arXiv ML reports that arXiv:2607.22766v1 Announce Type: new Abstract: The alignment of Large Language Models (LLMs) is increasingly bottlenecked by data quality.
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