Summary of Crowdsourcing with Difficulty: a Bayesian Rating Model For Heterogeneous Items, by Seong Woo Han et al.
Crowdsourcing with Difficulty: A Bayesian Rating Model for Heterogeneous Items
by Seong Woo Han, Ozan Adıgüzel, Bob Carpenter
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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 This paper tackles the issue of biased and noisy “gold standards” used in training machine learning models. The authors identify limitations in Dawid and Skene’s popular crowdsourcing model, which adjusts for rater sensitivity and specificity but neglects distributional properties of rating data. To address this, they introduce a general-purpose measurement-error model that accounts for item-level effects like difficulty, discriminativeness, and guessability. They also show how to constrain the model to avoid or allow adversarial raters. The authors validate their model’s goodness of fit using posterior predictive checks. Notably, Dawid and Skene’s model is rejected, whereas the new model, which adjusts for item heterogeneity, is not rejected. This work is demonstrated on two well-studied datasets: binary rating data for caries in dental X-rays and implication in natural language. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a problem with training machine learning models. Right now, people use “gold standards” that are often biased or noisy. The authors of this paper think that the popular crowdsourcing model by Dawid and Skene is good, but it has some big limitations. They want to make a better model that takes into account how different things are rated. This new model can even deal with people who try to cheat! The authors tested their model on two important datasets: one for detecting cavities in X-rays and another for understanding natural language. |
Keywords
* Artificial intelligence * Machine learning
