Clean First, Align Later: Benchmarking Preference Data Cleaning for Reliable LLM Alignment

Data quality matters more than previously recognized: The paper presents the first comprehensive benchmark showing that cleaning noisy human feedback significantly impacts how well language models align with human preferences, highlighting that data preprocessing is a crucial but often overlooked step in responsible AI development.

No single cleaning method works universally: The study evaluated 13 different automated data cleaning approaches and found that their effectiveness varies across different datasets, model architectures, and optimization algorithms, suggesting that organizations may need to test multiple strategies rather than relying on one-size-fits-all solutions.

Human feedback inconsistency is a widespread problem: The research identifies that human feedback used to train reward models is "often noisy or inconsistent," which can degrade alignment quality—a finding that underscores the practical challenges in collecting reliable preference data at scale.

Systematic evaluation enables better AI governance: By establishing PrefCleanBench as a standardized protocol for assessing cleaning methods, the paper provides a reproducible framework that organizations can use to make principled decisions about data quality, supporting more transparent and accountable AI development practices.

Preprocessing deserves greater investment: The work demonstrates that improving data quality through cleaning methods is a practical lever for responsible AI development, suggesting that resources devoted to data curation and validation may yield significant improvements in model alignment with human values.