Summary of Fastgas: Fast Graph-based Annotation Selection For In-context Learning, by Zihan Chen et al.
FastGAS: Fast Graph-based Annotation Selection for In-Context Learning
by Zihan Chen, Song Wang, Cong Shen, Jundong Li
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper proposes FastGAS, a graph-based method for efficiently selecting high-quality instances as prompts for large language models (LLMs) in-context learning (ICL). Existing methods select a subset of unlabeled examples for annotation, but these approaches are often complex and time-consuming. FastGAS constructs a data similarity graph, partitions it into pieces using a graph partitioning algorithm, and then selects representative nodes within each piece. This approach not only outperforms prior methods on various tasks but also significantly reduces selection time. The method is demonstrated to be effective in larger LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In-context learning helps large language models learn new tasks by using training instances as prompts. To make this process more efficient, researchers have proposed ways to select the best examples to annotate and use as prompts. However, these methods can take a long time because they are complex. This paper presents a new method called FastGAS that quickly finds the most useful examples while keeping computer time low. FastGAS works by building a graph of similar data points, breaking it down into smaller groups, and then picking the best points from each group. |
