Summary of Abstraction Alignment: Comparing Model-learned and Human-encoded Conceptual Relationships, by Angie Boggust and Hyemin Bang and Hendrik Strobelt and Arvind Satyanarayan
Abstraction Alignment: Comparing Model-Learned and Human-Encoded Conceptual Relationships
by Angie Boggust, Hyemin Bang, Hendrik Strobelt, Arvind Satyanarayan
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
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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 introduces a new methodology called abstraction alignment to assess whether machine learning models have learned human-aligned abstractions that enable generalization to new data. The approach compares a model’s behavior against formal human knowledge represented as an abstraction graph, which externalizes domain-specific concepts spanning multiple levels of abstraction. Abstraction alignment measures the alignment of a model’s uncertainty with the human abstractions and allows for testing alignment hypotheses across entire datasets. The methodology is evaluated with experts, demonstrating its ability to differentiate between seemingly similar errors, improve model-quality metrics, and uncover improvements to current human abstractions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how machine learning models work by showing that they learn concepts from humans in a way that’s aligned with our own thinking. It does this by comparing what the model has learned to what we know about a particular topic or field. The results show that this approach can help us identify problems with the model and even improve its performance. |
Keywords
» Artificial intelligence » Alignment » Generalization » Machine learning
