Summary of One Size Doesn’t Fit All: Predicting the Number Of Examples For In-context Learning, by Manish Chandra et al.
One size doesn’t fit all: Predicting the Number of Examples for In-Context Learning
by Manish Chandra, Debasis Ganguly, Iadh Ounis
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 Medium Difficulty summary: This paper presents an innovative approach to in-context learning (ICL), which aims to enhance the performance of language models on downstream tasks. The conventional ICL methods use a fixed number of localized examples for each data instance, but our work introduces a dynamic prediction mechanism that adjusts the number of examples based on the specific instance. We employ a multi-label classifier to determine the optimal number of examples (k) needed for correct k-shot predictions. Our experiments demonstrate that this adaptive in-context learning (AICL) outperforms standard ICL by up to 17% on various text classification benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about making language models work better. Right now, these models need a lot of information to do tasks correctly. The researchers developed a new way to give the models the right amount of information. They used a special tool that looks at each piece of information and decides how much more information it needs. This new approach worked really well, improving results by up to 17%. The team tested this method on different types of text classification tasks and found that it outperformed the old way. |
Keywords
* Artificial intelligence * Text classification
