11 citations · 22 across the 11 of their papers we have counts for
11 papers
On the Mutual Influence of Gender and Occupation in LLM Representations
Haozhe An, Connor Baumler, Abhilasha Sancheti +1
We examine LLM representations of gender for first names in various occupational contexts to study how occupations and the gender perception of first names in LLMs influence each o…
Susu Box or Piggy Bank: Assessing Cultural Commonsense Knowledge between Ghana and the U.S
Christabel Acquaye, Haozhe An, Rachel Rudinger
Recent work has highlighted the culturally-contingent nature of commonsense knowledge. We introduce AMAMMER, a test set of 525 multiple-choice questions designed to evaluate the…
On the Influence of Gender and Race in Romantic Relationship Prediction from Large Language Models
Abhilasha Sancheti, Haozhe An, Rachel Rudinger
We study the presence of heteronormative biases and prejudice against interracial romantic relationships in large language models by performing controlled name-replacement experime…
Do Large Language Models Discriminate in Hiring Decisions on the Basis of Race, Ethnicity, and Gender?
Haozhe An, Christabel Acquaye, Colin Wang +2
We examine whether large language models (LLMs) exhibit race- and gender-based name discrimination in hiring decisions, similar to classic findings in the social sciences (Bertrand…
Nichelle and Nancy: The Influence of Demographic Attributes and Tokenization Length on First Name Biases
Haozhe An, Rachel Rudinger
Through the use of first name substitution experiments, prior research has demonstrated the tendency of social commonsense reasoning models to systematically exhibit social biases…
SODAPOP: Open-Ended Discovery of Social Biases in Social Commonsense Reasoning Models
Haozhe An, Zongxia Li, Jieyu Zhao +1
A common limitation of diagnostic tests for detecting social biases in NLP models is that they may only detect stereotypic associations that are pre-specified by the designer of th…
Exploring the Common Principal Subspace of Deep Features in Neural Networks
Haoran Liu, Haoyi Xiong, Yaqing Wang +3
We find that different Deep Neural Networks (DNNs) trained with the same dataset share a common principal subspace in latent spaces, no matter in which architectures (e.g., Convolu…
XMixup: Efficient Transfer Learning with Auxiliary Samples by Cross-domain Mixup
Xingjian Li, Haoyi Xiong, Haozhe An +2
Transferring knowledge from large source datasets is an effective way to fine-tune the deep neural networks of the target task with a small sample size. A great number of algorithm…
RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr
Xingjian Li, Haoyi Xiong, Haozhe An +2
Fine-tuning the deep convolution neural network(CNN) using a pre-trained model helps transfer knowledge learned from larger datasets to the target task. While the accuracy could be…
The Weather Impacts the Outbreak of COVID-19 in Mainland China
Siyu Huang, Ji Liu, Haoyi Xiong +3
Recent literature has suggested that climate conditions have considerably significant influences on the transmission of coronavirus COVID-19. However, there is a lack of comprehens…
COLAM: Co-Learning of Deep Neural Networks and Soft Labels via Alternating Minimization
Xingjian Li, Haoyi Xiong, Haozhe An +2
Softening labels of training datasets with respect to data representations has been frequently used to improve the training of deep neural networks (DNNs). While such a practice ha…