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No Word Embedding Model Is Perfect: Evaluating the Representation
  Accuracy for Social Bias in the Media

No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media

7 November 2022
Maximilian Spliethover
Maximilian Keiff
Henning Wachsmuth
ArXivPDFHTML

Papers citing "No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media"

6 / 6 papers shown
Title
Measuring Political Bias in Large Language Models: What Is Said and How
  It Is Said
Measuring Political Bias in Large Language Models: What Is Said and How It Is Said
Yejin Bang
Delong Chen
Nayeon Lee
Pascale Fung
23
25
0
27 Mar 2024
A Note on Bias to Complete
A Note on Bias to Complete
Jia Xu
Mona Diab
39
2
0
18 Feb 2024
Sociodemographic Bias in Language Models: A Survey and Forward Path
Sociodemographic Bias in Language Models: A Survey and Forward Path
Vipul Gupta
Pranav Narayanan Venkit
Shomir Wilson
R. Passonneau
42
19
0
13 Jun 2023
Low Frequency Names Exhibit Bias and Overfitting in Contextualizing
  Language Models
Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Models
Robert Wolfe
Aylin Caliskan
85
51
0
01 Oct 2021
Controlled Neural Sentence-Level Reframing of News Articles
Controlled Neural Sentence-Level Reframing of News Articles
Wei-Fan Chen
Khalid Al Khatib
Benno Stein
Henning Wachsmuth
33
13
0
10 Sep 2021
Efficient Estimation of Word Representations in Vector Space
Efficient Estimation of Word Representations in Vector Space
Tomáš Mikolov
Kai Chen
G. Corrado
J. Dean
3DV
228
31,150
0
16 Jan 2013
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