Summary of Demonstration Selection For In-context Learning Via Reinforcement Learning, by Xubin Wang et al.
Demonstration Selection for In-Context Learning via Reinforcement Learning
by Xubin Wang, Jianfei Wu, Yichen Yuan, Mingzhe Li, Deyu Cai, Weijia Jia
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 presents an innovative approach called Relevance-Diversity Enhanced Selection (RDES), which leverages reinforcement learning to optimize the selection of diverse reference demonstrations for text classification tasks using Large Language Models (LLMs). The RDES method employs a Q-learning framework to dynamically identify demonstrations that maximize both diversity and relevance to the classification objective. This approach ensures a balanced representation of reference data, leading to improved classification accuracy. Experimental results on four benchmark datasets and involving 12 LLMs show that RDES significantly enhances classification accuracy compared to ten established baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence to help computers learn from examples better. It presents a new way to choose the right examples for a computer to learn from, which makes it more accurate at doing tasks like text classification. The method uses a special type of learning called reinforcement learning to pick examples that are both different and relevant to what the computer is trying to do. This helps the computer learn faster and better. |
Keywords
» Artificial intelligence » Classification » Reinforcement learning » Text classification
