GRASP: A Disagreement Analysis Framework to Assess Group Associations in Perspectives

doi:doi:10.57702/hlmr55n3

GRASP: A Disagreement Analysis Framework to Assess Group Associations in Perspectives

Human annotation plays a core role in machine learning — annotations for supervised models, safety guardrails for generative models, and human feedback for reinforcement learning, to cite a few avenues. However, the fact that many of these human annotations are inherently subjective is often overlooked.

BibTex:

@dataset{Vinodkumar_Prabhakaran_and_Christopher_M_Homan_and_Lora_Aroyo_and_Aida_Mostafazadeh_Davani_and_Alicia_Parrish_and_Alex_Taylor_and_Mark_Díaz_and_Ding_Wang_and_Gregory_Serapio-García_2024,
    abstract = {Human annotation plays a core role in machine learning — annotations for supervised models, safety guardrails for generative models, and human feedback for reinforcement learning, to cite a few avenues. However, the fact that many of these human annotations are inherently subjective is often overlooked.},
    author = {Vinodkumar Prabhakaran and Christopher M. Homan and Lora Aroyo and Aida Mostafazadeh Davani and Alicia Parrish and Alex Taylor and Mark Díaz and Ding Wang and Gregory Serapio-García},
    doi = {10.57702/hlmr55n3},
    institution = {No Organization},
    keyword = {'disagreement analysis', 'group associations', 'human annotation', 'machine learning', 'natural language processing', 'social media analysis'},
    month = {dec},
    publisher = {TIB},
    title = {GRASP: A Disagreement Analysis Framework to Assess Group Associations in Perspectives},
    url = {https://service.tib.eu/ldmservice/dataset/grasp--a-disagreement-analysis-framework-to-assess-group-associations-in-perspectives},
    year = {2024}
}