Summary of A Three-phases Sft Hybrid Model Integrated Strong Prior Module and Data Overlap Estimation in the Eduation Context, by Zhangquan Chen et al.
A Three-Phases SFT Hybrid Model Integrated Strong Prior Module and Data Overlap Estimation in the Eduation Context
by Zhangquan Chen, Chunjiang Liu, Haobin Duan
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an end-to-end prior-based three-phases supervised fine-tuned model that outperforms traditional fine-tuning methods in educational knowledge disassembly and incremental guided output. The model uses a sampler and overlap estimation neural network for robust data classification, and pre-trained models are fine-tuned in batches using the LORA method. A prior module is designed to incorporate system prompts, vector databases, and abstract syntax trees for task segmentation. Compression and regularization constraints are applied to the prior-based fine-tuned model, followed by text filtering at the output end. The model achieves state-of-the-art results on code abilities (75.10% on HumanEval @pass 1) and maintains strong conversational capabilities on MMLU, C-Eval, and AGIEval dialogue evaluation benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to understand educational knowledge by breaking it down into smaller pieces and guiding students step-by-step through the learning process. The method uses special computer algorithms and neural networks to help classify data and fine-tune pre-trained models for better results. The approach combines different parts, like system prompts and abstract syntax trees, to create a comprehensive framework for educational knowledge disassembly and incremental guided output. The model is tested on various benchmarks and achieves impressive results in code abilities and conversational capabilities. |
Keywords
* Artificial intelligence * Classification * Fine tuning * Lora * Neural network * Regularization * Supervised * Syntax
