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20182026
most citedBOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation

208 citations · 310 across the 19 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

cs.CL20225 cited

Is the Elephant Flying? Resolving Ambiguities in Text-to-Image Generative Models

Ninareh Mehrabi, Palash Goyal, Apurv Verma +7

Natural language often contains ambiguities that can lead to misinterpretation and miscommunication. While humans can handle ambiguities effectively by asking clarifying questions…

cs.CL2022

An Analysis of the Effects of Decoding Algorithms on Fairness in Open-Ended Language Generation

Jwala Dhamala, Varun Kumar, Rahul Gupta +2

Several prior works have shown that language models (LMs) can generate text containing harmful social biases and stereotypes. While decoding algorithms play a central role in deter…

cs.CL202210 cited

On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations

Yang Trista Cao, Yada Pruksachatkun, Kai-Wei Chang +4

Multiple metrics have been introduced to measure fairness in various natural language processing tasks. These metrics can be roughly categorized into two categories: 1) \emph{extri…

cs.CL20222 cited

Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal

Umang Gupta, Jwala Dhamala, Varun Kumar +7

Language models excel at generating coherent text, and model compression techniques such as knowledge distillation have enabled their use in resource-constrained settings. However,…

cs.LG2022

Measuring Fairness of Text Classifiers via Prediction Sensitivity

Satyapriya Krishna, Rahul Gupta, Apurv Verma +3

With the rapid growth in language processing applications, fairness has emerged as an important consideration in data-driven solutions. Although various fairness definitions have b…