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arXiv · 2502.07312

OpenGrok: Enhancing SNS Data Processing with Distilled Knowledge and Mask-like Mechanisms

Lumen AI·Zaozhuang No. 28 Middle School·Shihao Ji·Zihui Song·Fucheng Zhong·Jisen Jia·Zhaobo Wu·Zheyi Cao·Tianhao Xu
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Abstract

This report details Lumen Labs' novel approach to processing Social Networking Service (SNS) data. We leverage knowledge distillation, specifically a simple distillation method inspired by DeepSeek-R1's CoT acquisition, combined with prompt hacking, to extract valuable training data from the Grok model. This data is then used to fine-tune a Phi-3-mini model, augmented with a mask-like mechanism specifically designed for handling the nuances of SNS data. Our method demonstrates state-of-the-art (SOTA) performance on several SNS data processing tasks, outperforming existing models like Grok, Phi-3, and GPT-4. We provide a comprehensive analysis of our approach, including mathematical formulations, engineering details, ablation studies, and comparative evaluations.

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Lumen AI, Zaozhuang No. 28 Middle School, Shihao Ji, Zihui Song, Fucheng Zhong, Jisen Jia, Zhaobo Wu, Zheyi Cao, Tianhao Xu. 2025-02-11. OpenGrok: Enhancing SNS Data Processing with Distilled Knowledge and Mask-like Mechanisms. https://arxiv.org/abs/2502.07312

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