Core technologies for data quality control
SimpleDeepSearcher utilizes advanced data filtering techniques to ensure the quality of training data, which is one of its significant advantages over similar tools.
- Multi-dimensional screeningThe response_curation.py script implements a filtering process based on multiple criteria, such as question difficulty, inference path length, and search effectiveness, and the filtered data is stored in the cache/curated_data directory.
- Quality Indicators: The system evaluates the overall quality of each training sample, retaining data that can truly improve model performance, discarding inefficient or misleading samples, and dramatically improving training efficiency.
- Data Processing Flow: It consists of three main parts: initial data generation, diverse sampling, and multiple rounds of screening and optimization to ensure the representativeness and effectiveness of the final training set.
This stringent data quality control mechanism enables SimpleDeepSearcher to fine-tune large-scale models like QWEN2.5-32B using only 871 high-quality samples, which significantly reduces training costs and demand for computing resources.
This answer comes from the articleSimpleDeepSearcher: An Intelligent Retrieval Tool for Augmenting Large Language Models with Web SearchThe
May not be reproduced without permission:AI productivity tools " SimpleDeepSearcher's data filtering technique ensures high quality training data
