Prediction-powered inference
PulseAugur coverage of Prediction-powered inference — every cluster mentioning Prediction-powered inference across labs, papers, and developer communities, ranked by signal.
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New framework demystifies Prediction-Powered Inference for valid statistical analysis
A new arXiv paper by Yilin Song and colleagues introduces Prediction-Powered Inference (PPI), a framework designed to improve statistical efficiency by incorporating machine learning predictions into analyses. The paper…
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New research quantifies cost of per-class coverage under distribution shift
Researchers have characterized the cost of achieving valid per-class coverage in recognition systems when distribution shift occurs between training and testing data. They found that while split conformal prediction mai…
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New framework optimizes LLM-augmented surveys using human respondent allocation
A new research paper published on arXiv introduces a framework for optimizing survey design when using large language models (LLMs) for response generation. The framework addresses the challenge of LLM accuracy variabil…
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New paper analyzes Prediction-Powered Inference, finding no universal 'free lunch'
A new paper titled "No Free Lunch: Non-Asymptotic Analysis of Prediction-Powered Inference" analyzes the effectiveness of Prediction-Powered Inference (PPI) strategies. The research provides a finite-sample analysis of …
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LLMs improve ranking evaluation with new reliability methods
Two new research papers introduce methods to improve the reliability of Large Language Models (LLMs) in ranking tasks. One paper, PRECISE, uses Prediction-Powered Inference to combine human and LLM judgments, reducing e…
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New methods improve LLM evaluation accuracy with AI and human insights
Researchers have developed new methods to improve the accuracy and calibration of Large Language Model (LLM) evaluations. One approach, Conformal Elo Estimation, uses LLM judgments to estimate Elo ratings, achieving res…
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New MEC method enhances semi-supervised inference with better uncertainty quantification
Researchers have developed a new method called Machine-Learning-Assisted Generalized Entropy Calibration (MEC) to improve semi-supervised inference and uncertainty quantification. MEC is a cross-fitted, calibration-weig…