Summary of How Many Ratings Per Item Are Necessary For Reliable Significance Testing?, by Christopher Homan et al.
How Many Ratings per Item are Necessary for Reliable Significance Testing?
by Christopher Homan, Flip Korn, Chris Welty
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 methods for evaluating machine learning models’ performance by determining whether an evaluation dataset has enough responses per item to reliably compare model performances. It highlights the limitations of traditional accuracy, precision, and recall metrics due to AI models’ stochasticity and human raters’ disagreements. The authors apply their methods to existing gold standard test sets with multiple responses per item, showing that most datasets are insufficient for reliable performance comparisons. They also propose a method to estimate the required number of responses per item in hypothetical datasets with similar response distributions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how we evaluate machine learning models. Right now, we use simple metrics like accuracy and precision to compare models. But AI models can behave differently each time they’re used, and human raters don’t always agree on what’s correct. The authors suggest new methods to figure out if an evaluation dataset has enough responses to accurately compare model performances. They tested these methods on existing datasets and found that most of them have too few responses. They also proposed a way to estimate how many responses are needed for hypothetical datasets. |
Keywords
» Artificial intelligence » Machine learning » Precision » Recall
