Summary of The Power Of Adaptation: Boosting In-context Learning Through Adaptive Prompting, by Shuzhang Cai et al.
The Power of Adaptation: Boosting In-Context Learning through Adaptive Prompting
by Shuzhang Cai, Twumasi Mensah-Boateng, Xander Kuksov, Jing Yuan, Shaojie Tang
First submitted to arxiv on: 23 Dec 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 In this research paper, the authors propose an innovative approach to enhance the performance of Large Language Models (LLMs) on complex reasoning problems. By leveraging in-context learning and adaptive exemplar selection, the proposed method, Adaptive-Prompt, demonstrates significant improvements across various reasoning tasks. This medium-difficulty summary highlights the key contributions, methodology, and results of the paper, including its potential applications in natural language processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs are super smart computers that can solve many language-related problems. They’re really good at figuring out solutions to complex puzzles. To help them get even better, researchers use something called “in-context learning”. This is like giving the computer a step-by-step guide on how to answer questions correctly. The problem is finding the right examples for the computer to learn from. Currently, most studies pick some examples and then stop. But this might not be the best way because it can lead to lots of repetitive information that doesn’t help the computer learn much. In this study, the researchers came up with a new approach called “Adaptive-Prompt”. It chooses examples based on how well the computer is doing, making it more effective. |
Keywords
» Artificial intelligence » Natural language processing » Prompt
