Search Results for author: Jwala Dhamala

Found 21 papers, 4 papers with code

Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies

no code implementations19 Dec 2023 Anaelia Ovalle, Ninareh Mehrabi, Palash Goyal, Jwala Dhamala, Kai-Wei Chang, Richard Zemel, Aram Galstyan, Yuval Pinter, Rahul Gupta

Our paper is the first to link LLM misgendering to tokenization and deficient neopronoun grammar, indicating that LLMs unable to correctly treat neopronouns as pronouns are more prone to misgender.

JAB: Joint Adversarial Prompting and Belief Augmentation

no code implementations16 Nov 2023 Ninareh Mehrabi, Palash Goyal, Anil Ramakrishna, Jwala Dhamala, Shalini Ghosh, Richard Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta

With the recent surge of language models in different applications, attention to safety and robustness of these models has gained significant importance.

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

no code implementations7 Oct 2022 Jwala Dhamala, Varun Kumar, Rahul Gupta, Kai-Wei Chang, Aram Galstyan

We present a systematic analysis of the impact of decoding algorithms on LM fairness, and analyze the trade-off between fairness, diversity and quality.

Diversity Fairness +1

Measuring Fairness of Text Classifiers via Prediction Sensitivity

no code implementations ACL 2022 Satyapriya Krishna, Rahul Gupta, Apurv Verma, Jwala Dhamala, Yada Pruksachatkun, Kai-Wei Chang

With the rapid growth in language processing applications, fairness has emerged as an important consideration in data-driven solutions.

Attribute counterfactual +3

Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning

no code implementations13 Oct 2021 Md Shakil Zaman, Jwala Dhamala, Pradeep Bajracharya, John L. Sapp, B. Milan Horacek, Katherine C. Wu, Natalia A. Trayanova, Linwei Wang

In this paper, we present a Bayesian active learning method to directly approximate the posterior pdf function of cardiac model parameters, in which we intelligently select training points to query the simulation model in order to learn the posterior pdf using a small number of samples.

Active Learning

Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification

no code implementations Findings (ACL) 2021 Yada Pruksachatkun, Satyapriya Krishna, Jwala Dhamala, Rahul Gupta, Kai-Wei Chang

Existing bias mitigation methods to reduce disparities in model outcomes across cohorts have focused on data augmentation, debiasing model embeddings, or adding fairness-based optimization objectives during training.

Data Augmentation Fairness +2

BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation

1 code implementation27 Jan 2021 Jwala Dhamala, Tony Sun, Varun Kumar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei Chang, Rahul Gupta

To systematically study and benchmark social biases in open-ended language generation, we introduce the Bias in Open-Ended Language Generation Dataset (BOLD), a large-scale dataset that consists of 23, 679 English text generation prompts for bias benchmarking across five domains: profession, gender, race, religion, and political ideology.

Benchmarking Text Generation

Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks

no code implementations EMNLP (insights) 2020 Ansel MacLaughlin, Jwala Dhamala, Anoop Kumar, Sriram Venkatapathy, Ragav Venkatesan, Rahul Gupta

Neural Architecture Search (NAS) methods, which automatically learn entire neural model or individual neural cell architectures, have recently achieved competitive or state-of-the-art (SOTA) performance on variety of natural language processing and computer vision tasks, including language modeling, natural language inference, and image classification.

Image Classification Language Modelling +7

Learning Geometry-Dependent and Physics-Based Inverse Image Reconstruction

no code implementations18 Jul 2020 Xiajun Jiang, Sandesh Ghimire, Jwala Dhamala, Zhiyuan Li, Prashnna Kumar Gyawali, Linwei Wang

However, many reconstruction problems involve imaging physics that are dependent on the underlying non-Euclidean geometry.

Image Reconstruction

Quantifying the Uncertainty in Model Parameters Using Gaussian Process-Based Markov Chain Monte Carlo: An Application to Cardiac Electrophysiological Models

no code implementations2 Jun 2020 Jwala Dhamala, John L. Sapp, B. Milan Horácek, Linwei Wang

However, by sampling from an approximation of the exact posterior probability density function (pdf) of the parameters, the efficiency is gained at the expense of sampling accuracy.

Computational Efficiency

Bayesian Optimization on Large Graphs via a Graph Convolutional Generative Model: Application in Cardiac Model Personalization

1 code implementation1 Jul 2019 Jwala Dhamala, Sandesh Ghimire, John L. Sapp, B. Milan Horacek, Linwei Wang

In this paper, we present a novel graph convolutional VAE to allow generative modeling of non-Euclidean data, and utilize it to embed Bayesian optimization of large graphs into a small latent space.

Bayesian Optimization

Generative Modeling and Inverse Imaging of Cardiac Transmembrane Potential

no code implementations12 May 2019 Sandesh Ghimire, Jwala Dhamala, Prashnna Kumar Gyawali, John L. Sapp, B. Milan Horacek, Linwei Wang

We introduce a novel model-constrained inference framework that replaces conventional physiological models with a deep generative model trained to generate TMP sequences from low-dimensional generative factors.

Decoder

Cannot find the paper you are looking for? You can Submit a new open access paper.