Summary of Step-on-feet Tuning: Scaling Self-alignment Of Llms Via Bootstrapping, by Haoyu Wang et al.
Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping
by Haoyu Wang, Guozheng Ma, Ziqiao Meng, Zeyu Qin, Li Shen, Zhong Zhang, Bingzhe Wu, Liu Liu, Yatao Bian, Tingyang Xu, Xueqian Wang, Peilin Zhao
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 explores the effect of multi-time bootstrapping self-alignment on large language models. The authors find that bootstrapping self-alignment outperforms the single-round approach, ensuring data diversity through in-context learning. To further improve performance, they investigate and adjust the training order of data, leading to enhanced model capabilities. Building upon these findings, the researchers propose Step-On-Feet Tuning (SOFT) and SOFT+, which leverage the model’s continually enhanced few-shot ability to boost zero or one-shot performance. Experimental results demonstrate the efficiency of SOFT (SOFT+) across various classification and generation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can make language models better by repeating a process called self-alignment multiple times. They find that doing it this way makes the model learn more from what it already knows, which helps it perform better on new tasks. The authors also propose some new techniques to further improve performance and show that these work well across different types of tasks. |
Keywords
» Artificial intelligence » Alignment » Bootstrapping » Classification » Few shot » One shot
