OpenGrok: Enhancing SNS Data Processing with Distilled Knowledge and Mask-like Mechanisms
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915145927622656 |
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| author | AI, Lumen School, Zaozhuang No. 28 Middle Ji, Shihao Song, Zihui Zhong, Fucheng Jia, Jisen Wu, Zhaobo Cao, Zheyi Xu, Tianhao |
| author_facet | AI, Lumen School, Zaozhuang No. 28 Middle Ji, Shihao Song, Zihui Zhong, Fucheng Jia, Jisen Wu, Zhaobo Cao, Zheyi Xu, Tianhao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07312 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OpenGrok: Enhancing SNS Data Processing with Distilled Knowledge and Mask-like Mechanisms AI, Lumen School, Zaozhuang No. 28 Middle Ji, Shihao Song, Zihui Zhong, Fucheng Jia, Jisen Wu, Zhaobo Cao, Zheyi Xu, Tianhao Machine Learning Artificial Intelligence 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. |
| title | OpenGrok: Enhancing SNS Data Processing with Distilled Knowledge and Mask-like Mechanisms |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2502.07312 |