Principle:Datajuicer Data juicer QA Calibration

Knowledge Sources	Data-Juicer
Domains	NLP, Data_Quality, LLM
Last Updated	2026-02-14 17:00 GMT

Overview

An LLM-based quality refinement technique that uses a stronger model to review and improve generated question-answer pairs.

Description

QA Calibration takes previously generated QA pairs and passes them through a (typically stronger) LLM for quality review and correction. The calibration model evaluates whether questions are clear and answerable, whether answers are accurate and complete, and rewrites them to improve quality. This acts as a second pass over generated data to catch errors, inconsistencies, and low-quality outputs from the initial generation step.

Usage

Use this principle after the initial QA generation step to improve data quality. It is most effective when using a stronger model for calibration than was used for generation.

Theoretical Basis

# Abstract algorithm (NOT real implementation)
for qa_pair in generated_dataset:
    # Format calibration prompt
    prompt = calibration_template.format(
        query=qa_pair['query'],
        response=qa_pair['response']
    )

    # Call calibration model
    calibrated = calibration_model.generate(prompt)

    # Parse and replace
    qa_pair['query'] = parse_calibrated_query(calibrated)
    qa_pair['response'] = parse_calibrated_response(calibrated)

Related Pages

Implemented By

• Implementation:Datajuicer_Data_juicer_CalibrateQAMapper_Process