Summary of Global-to-local Support Spectrums For Language Model Explainability, by Lucas Agussurja et al.
Global-to-Local Support Spectrums for Language Model Explainability
by Lucas Agussurja, Xinyang Lu, Bryan Kian Hsiang Low
First submitted to arxiv on: 12 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The paper proposes a novel approach to generating explanations for machine learning models, specifically focusing on support spectrums as an alternative to traditional methods like influence functions and representer points. The proposed method decouples existing approaches into global and local components, selecting relevant training points based on their proximity to the test point and class boundaries. This allows for tailored explanations that are specific to individual test points, rather than relying on static or approximate measures. The paper demonstrates the effectiveness of this approach in image classification and text generation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to understand how machine learning models make predictions. Right now, we don’t really know why certain models work well for some data but not others. This paper tries to change that by creating a new kind of explanation that can be tailored to specific test points. Instead of using old methods that focus on outliers or points near decision boundaries, this approach looks at how close training points are to each other and the class they belong to. It’s like finding the right clues to understand why a model is making certain predictions. |
Keywords
» Artificial intelligence » Image classification » Machine learning » Text generation
