CritiQ: Mining Data Quality Criteria from Human Preferences
Authors: name: Honglin Guo, name: Kai Lv, name: Qipeng Guo, name: Tianyi Liang, name: Zhiheng Xi, name: Demin Song, name: Qiuyinzhe Zhang, name: Yu Sun, name: Kai Chen, name: Xipeng Qiu, name: Tao Gui

Language model heavily depends on high-quality data for optimal performance.
Existing approaches rely on manually designed heuristics, the perplexity of
existing models, training classifiers, or careful prompt engineering, which
require significant expert experience and human annotation effort while
introduce biases. We introduce CritiQ, a novel data selection method that
automatically mines criteria from human preferences for data quality with only
sim30 human-annotated pairs and performs efficient data selection. The main
component, CritiQ Flow, employs a manager agent to evolve quality criteria and
worker agents to make pairwise judgments. We build a knowledge base that
extracts quality criteria from previous work to boost CritiQ Flow. Compared to
perplexity- and classifier- based methods, verbal criteria are more
interpretable and possess reusable value. After deriving the criteria, we train
the CritiQ Scorer to give quality scores and perform efficient data selection.
We demonstrate the effectiveness of our method in the code, math, and logic
domains, achieving high accuracy on human-annotated test sets. To validate the
quality of the selected data, we continually train Llama 3.1 models and observe
improved performance on downstream tasks compared to uniform sampling. Ablation
studies validate the benefits of the knowledge base and the reflection process.
We analyze how criteria evolve and the effectiveness of majority voting.