Summary of Bayesian Prediction-powered Inference, by R. Alex Hofer and Joshua Maynez and Bhuwan Dhingra and Adam Fisch and Amir Globerson and William W. Cohen
Bayesian Prediction-Powered Inference
by R. Alex Hofer, Joshua Maynez, Bhuwan Dhingra, Adam Fisch, Amir Globerson, William W. Cohen
First submitted to arxiv on: 9 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 In this paper, researchers introduce Prediction-powered Inference (PPI), a method that enhances statistical estimates using limited human-labeled data. By combining small amounts of human-labeled data with larger amounts of automatically labeled data, PPI provides tighter confidence intervals. The authors propose a Bayesian inference-based framework for developing new task-appropriate PPI methods and demonstrate improved PPI methods for autoraters providing discrete responses and those with non-linear score relationships. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a way to make predictions better by combining small amounts of human-labeled data with lots of automatically labeled data. The method, called Prediction-powered Inference (PPI), helps us get more accurate results. The authors created a framework for PPI that makes it easy to develop new methods and tested them on different kinds of rating tasks. |
Keywords
» Artificial intelligence » Bayesian inference » Inference
