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Found 3 papers, 1 papers with code
Understanding Subjectivity through the Lens of Motivational Context in Model-Generated Image Satisfaction
Image generation models are poised to become ubiquitous in a range of applications.
Modeling subjectivity (by Mimicking Annotator Annotation) in toxic comment identification across diverse communities
The prevalence and impact of toxic discussions online have made content moderation crucial. Automated systems can play a vital role in identifying toxicity, and reducing the reliance on human moderation. Nevertheless, identifying toxic comments for diverse communities continues to present challenges that are addressed in this paper. The two-part goal of this study is to(1)identify intuitive variances from annotator disagreement using quantitative analysis and (2)model the subjectivity of these viewpoints. To achieve our goal, we published a new dataset\footnote{\url{https://github. com/XXX}} with expert annotators' annotations and used two other public datasets to identify the subjectivity of toxicity. Then leveraging the Large Language Model(LLM), we evaluate the model's ability to mimic diverse viewpoints on toxicity by varying size of the training data and utilizing same set of annotators as the test set used during model training and a separate set of annotators as the test set. We conclude that subjectivity is evident across all annotator groups, demonstrating the shortcomings of majority-rule voting.
LaMDA: Language Models for Dialog Applications
We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding.
Ranked #113 on
Code Generation
on HumanEval
