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

TL;DR

This paper introduces a novel SNS data processing method combining knowledge distillation, prompt hacking, and a mask-like mechanism, achieving state-of-the-art results on multiple SNS tasks.

Contribution

It presents a new approach that integrates knowledge distillation and specialized mechanisms to enhance SNS data processing performance.

Findings

Achieved SOTA performance on SNS tasks

Outperformed models like Grok, Phi-3, and GPT-4

Provided detailed analysis and ablation studies

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.