Summary of Demonstration Notebook: Finding the Most Suited In-context Learning Example From Interactions, by Yiming Tang and Bin Dong
Demonstration Notebook: Finding the Most Suited In-Context Learning Example from Interactions
by Yiming Tang, Bin Dong
First submitted to arxiv on: 16 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
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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 research paper explores the concept of prompt engineering in large language models (LLMs), specifically focusing on in-context learning. The authors propose a novel approach called the “demonstration notebook” to automatically create and choose demonstrations tailored to each question, taking into account the inherent heterogeneity within datasets. By selecting appropriate demonstrations, the method outperforms existing methods for automatic demonstration construction and selection, achieving state-of-the-art results on several reasoning benchmarks. The approach also demonstrates its versatility in text summarization and prompt compression tasks. Additionally, the authors provide a rigorous analysis method to reveal the “demonstrative regime” of a demonstration, providing valuable insights into how demonstrations relate to different question types within a dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special prompts to help large language models (LLMs) learn better. The idea is to use previous interactions with the model to create personalized “demonstrations” that are tailored to each specific question. This approach outperforms existing methods and can even be used for other tasks like summarizing text or compressing prompts. The researchers also came up with a way to analyze how well different demonstrations work, which could help us better understand how LLMs learn. |
Keywords
» Artificial intelligence » Prompt » Summarization
