Bontedance introduces QuadMix: a unified AI AI framework for data quality and diversity in LLM pre-audit

The preprocessing efficiency and generalization of the Large Language Model (LLM) is significantly influenced by the quality and diversity of the basic training corpus. Traditional data planning pipelines often view quality and diversity as separate goals, applying quality filtering, followed by domain balance. This sequential optimization ignores the complex interdependence between these factors. High-quality datasets often show domain bias, while diversified datasets can harm quality. In the context of a fixed training budget, it is urgent to optimize both dimensions simultaneously to maximize model performance. However, defining and co-optimizing quality and diversity remains a non-trivial challenge.

Wild introduces QuadMix

Bytedance proposes QuadMix, a unified data selection framework that systematically balances quality and diversity during LLM preprocessing. quadmix evaluates each data sample based on multiple quality standards and domain classifications and determines its sampling probability through parameterized functions. The framework uses proxy model experiments combined with LightGBM-based regression to predict downstream performance, enabling effective parameter optimization without exhaustive large-scale training. Experiments show that QuadMix has an average performance improvement of 7.2% in multiple benchmarks compared to methods that optimize quality and diversity respectively, emphasizing the effectiveness of the joint method.

QuadMix is ​​divided into three main stages: extraction, mass aggregation and quality diversity-aware sampling. Initially, each document is annotated with a domain tag and multiple quality scores. These scores are normalized and merged using domain-specific parameters to calculate a summary quality score. The document is then sampled based on a Sigmoid-based feature that prioritizes higher quality samples while maintaining domain balance through parameterized controls.

Optimization is performed by training thousands of proxy models across different parameter settings. Regression models trained through these proxy experiments can predict performance results, thereby determining the optimal sampling configuration. This approach allows structured exploration of high-dimensional parameter spaces, thereby aligning more closely with the expected downstream tasks.

quadmix offers several advantages:

• Unified data quality and optimization of domain diversity.
• Evaluate the adaptability of target selection to task-specific requirements through the agent.
• Computational efficiency is calculated by circumventing detailed full model retraining.
• Consistent downstream performance improvement without increasing the calculated budget.

Experimental results and insights

Verification experiments were performed using refined web datasets and the 53.30m parameter model was trained from scratch. QUADMIX is compared with several benchmarks including random selection, FineWeb-Edu, AskLlM, DCLM, DSIR, and Regmix. QuadMix always outperforms these methods, with an average score of 39.5% across 9 different benchmarks.

Key observations include:

• Joint optimization strategies always outperform siloed quality or diversity-centric approaches.
• The performance of the proxy model is closely related to the large-scale model results, thus verifying the effectiveness of the proxy-based approach.
• A mixture of data optimized for specific downstream tasks further enhances task performance.
• Combining multiple quality standards reduces inherent bias and improves overall model robustness.
• Expanding the diversity of tokens beyond a certain threshold reduces returns, which emphasizes the importance of select quality than pure quantity.

in conclusion

QuadMix provides a principled approach to data selection for LLM pre-audit to optimize the long-term challenges of data quality and diversity simultaneously. By integrating quality aggregation and domain-aware sampling into a unified framework and leveraging proxy-based optimization, QuadMix has established a scalable approach to improve LLM read-out efficiency. Despite the opportunity to make future improvements (such as refining parameter space and enhancing proxy model fidelity), QuadMix represents an important step towards a more systematic, effective data strategy to enable large-scale model development.

Check Paper. Also, don’t forget to follow us twitter And join us Telegram Channel and LinkedIn GrOUP. Don’t forget to join us 90K+ ml reddit.

🔥 [Register Now] Minicon Agesic AI Virtual Conference: Free Registration + Certificate of Attendance + 4-hour Short Event (May 21, 9am-1pm) + Hands-On the Workshop

Asif Razzaq is CEO of Marktechpost Media Inc. As a visionary entrepreneur and engineer, ASIF is committed to harnessing the potential of artificial intelligence to achieve social benefits. His recent effort is to launch Marktechpost, an artificial intelligence media platform that has an in-depth coverage of machine learning and deep learning news that can sound both technically, both through technical voices and be understood by a wide audience. The platform has over 2 million views per month, indicating its popularity among its audience.