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Found 5 papers, 2 papers with code
"Flex Tape Can't Fix That": Bias and Misinformation in Edited Language Models
We introduce a novel benchmark dataset, Seesaw-CF, for measuring bias-related harms of model editing and conduct the first in-depth investigation of how different weight-editing methods impact model bias.
Multilingual large language models leak human stereotypes across language boundaries
We evaluate human stereotypes and stereotypical associations manifested in multilingual large language models such as mBERT, mT5, and ChatGPT.
Analyzing and modeling network travel patterns during the Ukraine invasion using crowd-sourced pervasive traffic data
This paper examines the impact of the ongoing disruption on traffic behavior using analytics as well as zonal-based network models.
Theory-Grounded Measurement of U.S. Social Stereotypes in English Language Models
NLP models trained on text have been shown to reproduce human stereotypes, which can magnify harms to marginalized groups when systems are deployed at scale.
