Principle:Datajuicer Data juicer QA Optimization

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

Overview

An iterative LLM-based enhancement technique that rewrites question-answer pairs to increase complexity, diversity, and educational value.

Description

QA Optimization goes beyond calibration (error correction) to actively enhance QA pairs. It uses an LLM to rewrite questions to be more challenging or nuanced, expand answers with additional context or reasoning steps, and increase the overall instructional quality of the training data. This is similar to evol-instruct approaches where simpler instructions are evolved into more complex ones through LLM-guided rewriting.

Usage

Use this principle after initial generation and optional calibration to produce higher-quality training data. It can use either API-based or local HuggingFace models.

Theoretical Basis

# Abstract algorithm (NOT real implementation)
for qa_pair in dataset:
    # Construct optimization prompt
    prompt = optimization_template.format(
        query=qa_pair['query'],
        response=qa_pair['response'],
        optimization_goal='increase complexity and depth'
    )

    # Generate optimized version
    optimized = model.generate(prompt)

    # Parse and replace
    qa_pair['query'] = parse_optimized_query(optimized)
    qa_pair['response'] = parse_optimized_response(optimized)

Related Pages

Implemented By

• Implementation:Datajuicer_Data_juicer_OptimizeQAMapper_Process