GraphGen utilizesExpected calibration error(Expected Calibration Error, or ECE) as the core technical index to quantify the cognitive bias of the model. The specific realization process is divided into three stages:
- Predictive confidence analysis: As the model processes nodes in the knowledge graph, the system records the confidence of the model's answers to the relevant questions
- Verification of accuracy: Compare the predictions of the model with the standard facts in the knowledge graph and calculate the actual accuracy rate
- Error quantification: the degree of bias is calculated by the ECE formula (|confidence-accuracy| weighted average), usually with 0.1 set as the default threshold
The technological advantage is reflected in:dynamic labelingThe system flags knowledge points with ECE values above the threshold in real time;prioritizeImplement weighting for high-frequency error knowledge points;configurableAllows researchers to adjust the threshold sensitivity via YAML files. This quantitative-based diagnostic approach improves the efficiency of traditional manual labeling by about 801 TP3T.
This answer comes from the articleGraphGen: Fine-tuning Language Models Using Knowledge Graphs to Generate Synthetic DataThe
