OpenGrok: Enhancing SNS Data Processing with Distilled Knowledge and Mask-like Mechanisms
This addresses SNS data processing for users needing enhanced model performance, but it appears incremental as it builds on existing distillation and fine-tuning techniques.
The paper tackled processing Social Networking Service (SNS) data by using knowledge distillation from Grok and a mask-like mechanism to fine-tune a Phi-3-mini model, achieving state-of-the-art performance that outperformed models like Grok, Phi-3, and GPT-4 on several tasks.
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.