Summary of An Experimental Design Framework For Label-efficient Supervised Finetuning Of Large Language Models, by Gantavya Bhatt et al.
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models
by Gantavya Bhatt, Yifang Chen, Arnav M. Das, Jifan Zhang, Sang T. Truong, Stephen Mussmann, Yinglun Zhu, Jeffrey Bilmes, Simon S. Du, Kevin Jamieson, Jordan T. Ash, Robert D. Nowak
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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 In this paper, researchers explore ways to reduce the costs associated with training large language models (LLMs) while maintaining their impressive zero-shot generalization capabilities. Specifically, they investigate the use of experimental design techniques to optimize the labeling process for instruction datasets, which are crucial for fine-tuning LLMs. By implementing various existing and novel methods, the authors demonstrate that these techniques can significantly reduce the annotation costs required for SFT, achieving comparable performance with only 50% of the cost needed for random sampling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to make big language models smarter without needing as much help from humans. Right now, we have to teach them lots of things by giving them examples and saying what’s right or wrong. But that takes a lot of work and money! To solve this problem, scientists are trying different ways to pick which examples to use for teaching. They found some methods that work really well and don’t take up too many computer resources. |
Keywords
* Artificial intelligence * Fine tuning * Generalization * Zero shot
