9 citations · 19 across the 9 of their papers we have counts for
9 papers
Agree to Disagree? A Meta-Evaluation of LLM Misgendering
Arjun Subramonian, Vagrant Gautam, Preethi Seshadri +3
Numerous methods have been proposed to measure LLM misgendering, including probability-based evaluations (e.g., automatically with templatic sentences) and generation-based evaluat…
Small Changes, Large Consequences: Analyzing the Allocational Fairness of LLMs in Hiring Contexts
Preethi Seshadri, Hongyu Chen, Sameer Singh +1
Large language models (LLMs) are increasingly being deployed in high-stakes applications like hiring, yet their potential for unfair decision-making remains understudied in generat…
Learning Robust Representations for Communications over Interference-limited Channels
Shubham Paul, Sudharsan Senthil, Preethi Seshadri +2
In the context of cellular networks, users located at the periphery of cells are particularly vulnerable to substantial interference from neighbouring cells, which can be represent…
How Many Images Does It Take? Estimating Imitation Thresholds in Text-to-Image Models
Sahil Verma, Royi Rassin, Arnav Das +6
Text-to-image models are trained using large datasets of image-text pairs collected from the internet. These datasets often include copyrighted and private images. Training models…
Are Models Biased on Text without Gender-related Language?
Catarina G Belém, Preethi Seshadri, Yasaman Razeghi +1
Gender bias research has been pivotal in revealing undesirable behaviors in large language models, exposing serious gender stereotypes associated with occupations, and emotions. A…
The Bias Amplification Paradox in Text-to-Image Generation
Preethi Seshadri, Sameer Singh, Yanai Elazar
Bias amplification is a phenomenon in which models exacerbate biases or stereotypes present in the training data. In this paper, we study bias amplification in the text-to-image do…
Quantifying Social Biases Using Templates is Unreliable
Preethi Seshadri, Pouya Pezeshkpour, Sameer Singh
Recently, there has been an increase in efforts to understand how large language models (LLMs) propagate and amplify social biases. Several works have utilized templates for fairne…
Fonts-2-Handwriting: A Seed-Augment-Train framework for universal digit classification
Vinay Uday Prabhu, Sanghyun Han, Dian Ang Yap +3
In this paper, we propose a Seed-Augment-Train/Transfer (SAT) framework that contains a synthetic seed image dataset generation procedure for languages with different numeral syste…
Real Time Speckle Image De-Noising
D. Sachin Kumar, P. R. Seshadri, N. Vaishnav +1
The paper presents real time speckle de-noising based on activity computation algorithm and wavelet transform. Speckles arise in an image when laser light is reflected from an illu…