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 +4 more
2/11/2025
cs.LGcs.AI

No official code found. Best implementation: Lumen-Laboratory/OpenGrok (1 star, MIT, last commit 2025-02-08).

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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Code Implementations(1)

Lumen-Laboratory/OpenGrokCommunity100%
MIT

Run Grok on your local computer ⭐

10PythonFeb 8, 20251 years agoMIT
grokllmphi-3-miniprompt-hacktwitter+1 more

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Cite this paper

@article{ai2025opengrok,
  title  = {OpenGrok: Enhancing SNS Data Processing with Distilled Knowledge and Mask-like Mechanisms},
  author = {Lumen AI and Zaozhuang No. 28 Middle School and Shihao Ji and Zihui Song and Fucheng Zhong and Jisen Jia and Zhaobo Wu and Zheyi Cao and Tianhao Xu},
  year   = {2025},
  eprint = {2502.07312},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.07312v1}
}

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