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Found 7 papers, 4 papers with code
ViSAGe: A Global-Scale Analysis of Visual Stereotypes in Text-to-Image Generation
First, we show that stereotypical attributes in ViSAGe are thrice as likely to be present in generated images of corresponding identities as compared to other attributes, and that the offensiveness of these depictions is especially higher for identities from Africa, South America, and South East Asia.
SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Models
Stereotype benchmark datasets are crucial to detect and mitigate social stereotypes about groups of people in NLP models.
Transformer-based Models for Long-Form Document Matching: Challenges and Empirical Analysis
Firstly, the performance gain provided by transformer-based models comes at a steep cost - both in terms of the required training time and the resource (memory and energy) consumption.
CodeAttack: Code-Based Adversarial Attacks for Pre-trained Programming Language Models
Pre-trained programming language (PL) models (such as CodeT5, CodeBERT, GraphCodeBERT, etc.,) have the potential to automate software engineering tasks involving code understanding and code generation.
Supervised Contrastive Learning for Interpretable Long-Form Document Matching
When handling such long documents, there are three primary challenges: (i) the presence of different contexts for the same word throughout the document, (ii) small sections of contextually similar text between two documents, but dissimilar text in the remaining parts (this defies the basic understanding of "similarity"), and (iii) the coarse nature of a single global similarity measure which fails to capture the heterogeneity of the document content.
Fair Representation Learning using Interpolation Enabled Disentanglement
In this paper, we propose a novel method to address two key issues: (a) Can we simultaneously learn fair disentangled representations while ensuring the utility of the learned representation for downstream tasks, and (b)Can we provide theoretical insights into when the proposed approach will be both fair and accurate.
When does a compliment become sexist? Analysis and classification of ambivalent sexism using twitter data
Our work helps analyze and understand the much prevalent ambivalent sexism in social media.
