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Annotation Sensitivity: Training Data Collection Methods Affect Model Performance
23 November 2023
Christoph Kern
Stephanie Eckman
Jacob Beck
Rob Chew
Bolei Ma
Frauke Kreuter
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Papers citing
"Annotation Sensitivity: Training Data Collection Methods Affect Model Performance"
7 / 7 papers shown
Title
Correcting Annotator Bias in Training Data: Population-Aligned Instance Replication (PAIR)
Stephanie Eckman
Bolei Ma
Christoph Kern
Rob Chew
Barbara Plank
Frauke Kreuter
41
0
0
12 Jan 2025
A quest through interconnected datasets: lessons from highly-cited ICASSP papers
Cynthia C. S. Liem
Doğa Taşcılar
Andrew M. Demetriou
15
0
0
19 Sep 2024
Towards Estimating Personal Values in Song Lyrics
Andrew M. Demetriou
Jaehun Kim
Sandy Manolios
Cynthia C. S. Liem
24
0
0
22 Aug 2024
The Potential and Challenges of Evaluating Attitudes, Opinions, and Values in Large Language Models
Bolei Ma
Xinpeng Wang
Tiancheng Hu
Anna Haensch
Michael A. Hedderich
Barbara Plank
Frauke Kreuter
ALM
33
2
0
16 Jun 2024
Context Does Matter: Implications for Crowdsourced Evaluation Labels in Task-Oriented Dialogue Systems
Clemencia Siro
Mohammad Aliannejadi
Maarten de Rijke
27
3
0
15 Apr 2024
Position: Insights from Survey Methodology can Improve Training Data
Stephanie Eckman
Barbara Plank
Frauke Kreuter
SyDa
31
3
0
02 Mar 2024
Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets
Mor Geva
Yoav Goldberg
Jonathan Berant
237
319
0
21 Aug 2019
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