Search Results for author:
Found 9 papers, 4 papers with code
Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle*
Such inferences raise the risk of systematic biases in coreference resolution systems, including biases that can harm binary and non-binary trans and cis stakeholders.
Multilingual large language models leak human stereotypes across language boundaries
We evaluate human stereotypes and stereotypical associations manifested in multilingual large language models such as mBERT, mT5, and ChatGPT.
Toxicity Detection is NOT all you Need: Measuring the Gaps to Supporting Volunteer Content Moderators
Extensive efforts in automated approaches for content moderation have been focused on developing models to identify toxic, offensive, and hateful content with the aim of lightening the load for moderators.
What's Different between Visual Question Answering for Machine "Understanding" Versus for Accessibility?
We aim to answer this question by evaluating discrepancies between machine "understanding" datasets (VQA-v2) and accessibility datasets (VizWiz) by evaluating a variety of VQA models.
Theory-Grounded Measurement of U.S. Social Stereotypes in English Language Models
NLP models trained on text have been shown to reproduce human stereotypes, which can magnify harms to marginalized groups when systems are deployed at scale.
On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations
Multiple metrics have been introduced to measure fairness in various natural language processing tasks.
Toward Gender-Inclusive Coreference Resolution
Correctly resolving textual mentions of people fundamentally entails making inferences about those people.
Controlling the Specificity of Clarification Question Generation
Unlike comprehension-style questions, clarification questions look for some missing information in a given context.
