31 citations · 54 across the 12 of their papers we have counts for
13 papers
Agentic DAG-Orchestrated Planner Framework for Multi-Modal, Multi-Hop Question Answering in Hybrid Data Lakes
Kirushikesh D B, Manish Kesarwani, Nishtha Madaan +6
Enterprises increasingly need natural language (NL) question answering over hybrid data lakes that combine structured tables and unstructured documents. Current deployed solutions,…
LLMGuard: Guarding Against Unsafe LLM Behavior
Shubh Goyal, Medha Hira, Shubham Mishra +6
Although the rise of Large Language Models (LLMs) in enterprise settings brings new opportunities and capabilities, it also brings challenges, such as the risk of generating inappr…
Navigating the Structured What-If Spaces: Counterfactual Generation via Structured Diffusion
Nishtha Madaan, Srikanta Bedathur
Generating counterfactual explanations is one of the most effective approaches for uncovering the inner workings of black-box neural network models and building user trust. While r…
"Beware of deception": Detecting Half-Truth and Debunking it through Controlled Claim Editing
Sandeep Singamsetty, Nishtha Madaan, Sameep Mehta +2
The prevalence of half-truths, which are statements containing some truth but that are ultimately deceptive, has risen with the increasing use of the internet. To help combat this…
DetAIL : A Tool to Automatically Detect and Analyze Drift In Language
Nishtha Madaan, Adithya Manjunatha, Hrithik Nambiar +4
Machine learning and deep learning-based decision making has become part of today's software. The goal of this work is to ensure that machine learning and deep learning-based syste…
Plug and Play Counterfactual Text Generation for Model Robustness
Nishtha Madaan, Srikanta Bedathur, Diptikalyan Saha
Generating counterfactual test-cases is an important backbone for testing NLP models and making them as robust and reliable as traditional software. In generating the test-cases, a…
TransDrift: Modeling Word-Embedding Drift using Transformer
Nishtha Madaan, Prateek Chaudhury, Nishant Kumar +1
In modern NLP applications, word embeddings are a crucial backbone that can be readily shared across a number of tasks. However as the text distributions change and word semantics…
Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text
Nishtha Madaan, Inkit Padhi, Naveen Panwar +1
Machine Learning has seen tremendous growth recently, which has led to larger adoption of ML systems for educational assessments, credit risk, healthcare, employment, criminal just…
Fair Transfer of Multiple Style Attributes in Text
Karan Dabas, Nishtha Madan, Vijay Arya +3
To preserve anonymity and obfuscate their identity on online platforms users may morph their text and portray themselves as a different gender or demographic. Similarly, a chatbot…
Judging a Book by its Description : Analyzing Gender Stereotypes in the Man Bookers Prize Winning Fiction
Nishtha Madaan, Sameep Mehta, Shravika Mittal +1
The presence of gender stereotypes in many aspects of society is a well-known phenomenon. In this paper, we focus on studying and quantifying such stereotypes and bias in the Man B…
Generating Clues for Gender based Occupation De-biasing in Text
Nishtha Madaan, Gautam Singh, Sameep Mehta +2
Vast availability of text data has enabled widespread training and use of AI systems that not only learn and predict attributes from the text but also generate text automatically.…
Bollywood Movie Corpus for Text, Images and Videos
Nishtha Madaan, Sameep Mehta, Mayank Saxena +3
In past few years, several data-sets have been released for text and images. We present an approach to create the data-set for use in detecting and removing gender bias from text.…
Analyzing Gender Stereotyping in Bollywood Movies
Nishtha Madaan, Sameep Mehta, Taneea S Agrawaal +3
The presence of gender stereotypes in many aspects of society is a well-known phenomenon. In this paper, we focus on studying such stereotypes and bias in Hindi movie industry (Bol…