Hugging Face Trending Papers

In-Context Learning for the Imputation of Public Opinion Data with Large Language Models

Read the original on Hugging Face Trending Papers →

Large language models have been widely evaluated as simulators of individual survey responses. In practice, however, fully unobserved responses are rare; the dominant problem is partial non-response.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

arXiv Machine Learning
Jun 9

Partial Identification under Missing Data Using Weak Shadow Variables from Pretrained Models

arXiv:2602. 16061v2 Announce Type: replace-cross Abstract: Estimating population quantities such as mean outcomes from user feedback is fundamental to platform evaluation and social science, yet feedback is often missing not at random (MNAR): users with stronger opinions are more likely to respond, so standard estimators are biased and the estimand is not identified without additional assumptions.

By Hongyu Chen, David Simchi-Levi, Ruoxuan Xiong
arXiv Machine Learning
22h ago

One Pipeline, Many Transformers: Pattern-Specific Imputation Specialists for Tabular Missing Data

arXiv:2510. 02625v5 Announce Type: replace Abstract: Missing data in tabular datasets forces practitioners into a hard choice: deploy a general-purpose imputer that may perform poorly for the problem at hand, or wait for someone to design a specialized algorithm.

By Jacob Feitelberg, Dwaipayan Saha, Kyuseong Choi, Zaid Ahmad, Anish Agarwal, Raaz Dwivedi