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

Samuel Yeh, Sharon Li

Keywords: preference alignment, reliability of human feedback, data cleaning

Abstract: Human feedback plays a pivotal role in aligning large language models (LLMs) with human preferences. However, such feedback is often noisy or inconsistent, which can degrade the quality of reward models and hinder alignment. While various automated data cleaning methods have been proposed to mitigate this issue, a systematic evaluation of their effectiveness and generalizability remains lacking. To bridge this gap, we introduce the first comprehensive benchmark for evaluating 13 preference data cleaning methods in the context of LLM alignment. Our framework offers a standardized protocol to assess cleaning strategies in terms of alignment performance and generalizability across diverse datasets, model architectures, and optimization algorithms. By unifying disparate methods and rigorously comparing them, we uncover key factors that determine the success of data cleaning in alignment tasks. This benchmark lays the groundwork for principled and reproducible approaches to improving LLM alignment through better data quality—highlighting the crucial but underexplored role of data preprocessing in responsible AI development. We release modular implementations of all methods to catalyze further research: https://github.com/deeplearning-wisc/PrefCleanBench.

Code URL: https://github.com/deeplearning-wisc/PrefCleanBench

Primary Area: Evaluation (e.g., data collection methodology, data processing methodology, data analysis methodology, meta studies on data sources, extracting signals from data, replicability of data collection and data analysis and validity of metrics, validity of data collection experiments, human-in-the-loop for data collection, human-in-the-loop for data evaluation)

Submission Number: 481