Papers by Apurv Verma
Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal (2022.findings-acl)
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Umang Gupta, Jwala Dhamala, Varun Kumar, Apurv Verma, Yada Pruksachatkun, Satyapriya Krishna, Rahul Gupta, Kai-Wei Chang, Greg Ver Steeg, Aram Galstyan
| Challenge: | Language models excel at generating coherent text, but can be biased in multiple ways, including the unfounded association of male and female genders with gender-neutral professions. |
| Approach: | They propose to modify teacher probabilities and augment the training set to learn a fair model during knowledge distillation by modifying teacher probability and augmenting the training sets. |
| Outcome: | The proposed approach reduces gender disparity in open-ended text generated from the distilled and finetuned models with only a minor compromise in utility. |
Measuring Fairness of Text Classifiers via Prediction Sensitivity (2022.acl-long)
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| Challenge: | Existing fairness metrics are not yet available to measure the fairness of language processing systems. |
| Approach: | They propose a new metric which measures fairness in machine learning models based on the model’s prediction sensitivity to perturbations in input features. |
| Outcome: | The proposed metric can be linked with a specific notion of group fairness and individual fairness, and correlates well with humans’ perception of fairness. |
Resolving Ambiguities in Text-to-Image Generative Models (2023.acl-long)
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Ninareh Mehrabi, Palash Goyal, Apurv Verma, Jwala Dhamala, Varun Kumar, Qian Hu, Kai-Wei Chang, Richard Zemel, Aram Galstyan, Rahul Gupta
| Challenge: | ambiguities can lead to misinterpretation and miscommunication in natural language . resolving ambiguity is notoriously hard for machines . |
| Approach: | They propose a framework to disambiguate prompts given to generative models by soliciting clarifications from the end user. |
| Outcome: | The proposed framework generates more faithful images better aligned with user intention in the presence of ambiguities. |