Summary of Inherent Trade-offs Between Diversity and Stability in Multi-task Benchmarks, by Guanhua Zhang and Moritz Hardt
Inherent Trade-Offs between Diversity and Stability in Multi-Task Benchmarks
by Guanhua Zhang, Moritz Hardt
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed research explores the concept of multi-task benchmarks in machine learning through the lens of social choice theory. The study draws an analogy between benchmarks and electoral systems, where models are candidates and tasks are voters. This leads to a distinction between cardinal and ordinal benchmark systems. Cardinal systems aggregate numerical scores into one model ranking, while ordinal systems aggregate rankings for each task. The research applies Arrow’s impossibility theorem to ordinal benchmarks, highlighting their inherent limitations, particularly their sensitivity to the inclusion of irrelevant models. The study introduces new quantitative measures of diversity and sensitivity that are used to demonstrate a strong trade-off between diversity and sensitivity to irrelevant changes in existing multi-task benchmarks. Efficient approximation algorithms are developed for these measures, as exact computation is computationally challenging. Extensive experiments on seven cardinal benchmarks and eleven ordinal benchmarks show a clear trade-off between diversity and stability: The more diverse a multi-task benchmark, the more sensitive it is to trivial changes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research looks at how well machine learning models do when they’re asked to do many different tasks. It’s like having an election where multiple candidates are running for office. The study shows that there are two main types of benchmarks: cardinal and ordinal. Cardinal benchmarks give one overall score, while ordinal benchmarks rank each task separately. The research also uses a famous theorem from social choice theory to show that ordinal benchmarks have some major limitations. One of those limitations is that they’re easily affected by irrelevant changes to the tasks. The study introduces two new measures: diversity and sensitivity. Diversity shows how different the model’s rankings are across tasks, while sensitivity shows how much a small change in one task affects the overall ranking. The research finds that there’s a trade-off between these two things: as benchmarks get more diverse, they become more sensitive to changes. This means that if you want a benchmark that is very good at many tasks, it will also be affected by small changes. |
Keywords
» Artificial intelligence » Machine learning » Multi task
