Summary of Self-guide: Better Task-specific Instruction Following Via Self-synthetic Finetuning, by Chenyang Zhao et al.
SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning
by Chenyang Zhao, Xueying Jia, Vijay Viswanathan, Tongshuang Wu, Graham Neubig
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach, SELF-GUIDE, is proposed to improve the performance of large language models (LLMs) by finetuning them using task-specific input-output pairs synthesized from the student model itself. This multi-stage mechanism aims to overcome the limitations of existing methods that rely on state-of-the-art LLMs and annotated datasets. Experimental results on the Natural Instructions V2 benchmark show a significant improvement in performance, with an absolute gain of approximately 15% for classification tasks and 18% for generation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are amazing tools that can help us solve many problems when given the right instructions. However, these instructions often make the model less accurate than if it had been trained on lots of data specifically designed for the task. The solution proposed in this research is to create special training data just for the task we want the model to do well at. This helps the model learn how to do the task better, without needing any extra help from more powerful models. |
Keywords
» Artificial intelligence » Classification » Student model
