OpenGrok: Enhancing SNS Data Processing with Distilled Knowledge and Mask-like Mechanisms
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Abstract (arXiv)
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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- 2024Dataset Distillation via Knowledge Distillation: Towards Efficient Self-Supervised Pre-Training of Deep Networks
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- 2026Efficient Knowledge Distillation for LLMs: Offline Top-K Logits and a Fused Chunked KL Loss
- 2025Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor Attacks
- 2023FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection
- 2023Learning the Wrong Lessons: Inserting Trojans During Knowledge Distillation
- 2024Knowledge Distillation Using Frontier Open-source LLMs: Generalizability and the Role of Synthetic Data
- 2023A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness