You Are What You Annotate: Towards Better Models through Annotator Representations

24 May 2023

Papers citing "You Are What You Annotate: Towards Better Models through Annotator Representations"

6 / 6 papers shown

Title
Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation Disagreement Junyu Lu Kai Ma Kaichun Wang Kelaiti Xiao Roy Ka-Wei Lee Bo Xu Liang Yang Hongfei Lin 51 0 0 10 Feb 2025
Training and Evaluating with Human Label Variation: An Empirical Study Kemal Kurniawan Meladel Mistica Timothy Baldwin Jey Han Lau 67 0 0 03 Feb 2025
Cost-Efficient Subjective Task Annotation and Modeling through Few-Shot Annotator Adaptation Preni Golazizian Ali Omrani Alireza S. Ziabari Morteza Dehghani 25 1 0 21 Feb 2024
GRASP: A Disagreement Analysis Framework to Assess Group Associations in Perspectives Vinodkumar Prabhakaran Christopher Homan Lora Aroyo Aida Mostafazadeh Davani Alicia Parrish Alex S. Taylor Mark Díaz Ding Wang Greg Serapio-García 37 9 0 09 Nov 2023
Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators' Disagreement Elisa Leonardelli Stefano Menini Alessio Palmero Aprosio Marco Guerini Sara Tonelli 52 97 0 28 Sep 2021
Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets Mor Geva Yoav Goldberg Jonathan Berant 242 320 0 21 Aug 2019