activity
20202023
most citedPractice Makes a Solver Perfect: Data Augmentation for Math Word Problem Solvers

1 citations · 2 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV20231 cited

Improving Selective Visual Question Answering by Learning from Your Peers

Corentin Dancette, Spencer Whitehead, Rishabh Maheshwary +5

Despite advances in Visual Question Answering (VQA), the ability of models to assess their own correctness remains underexplored. Recent work has shown that VQA models, out-of-the-…

cs.CL20221 cited

Practice Makes a Solver Perfect: Data Augmentation for Math Word Problem Solvers

Vivek Kumar, Rishabh Maheshwary, Vikram Pudi

Existing Math Word Problem (MWP) solvers have achieved high accuracy on benchmark datasets. However, prior works have shown that such solvers do not generalize well and rely on sup…

cs.CL2021

Adversarial Examples for Evaluating Math Word Problem Solvers

Vivek Kumar, Rishabh Maheshwary, Vikram Pudi

Standard accuracy metrics have shown that Math Word Problem (MWP) solvers have achieved high performance on benchmark datasets. However, the extent to which existing MWP solvers tr…

cs.CL2021

A Strong Baseline for Query Efficient Attacks in a Black Box Setting

Rishabh Maheshwary, Saket Maheshwary, Vikram Pudi

Existing black box search methods have achieved high success rate in generating adversarial attacks against NLP models. However, such search methods are inefficient as they do not…

cs.CL2020

A Context Aware Approach for Generating Natural Language Attacks

Rishabh Maheshwary, Saket Maheshwary, Vikram Pudi

We study an important task of attacking natural language processing models in a black box setting. We propose an attack strategy that crafts semantically similar adversarial exampl…

cs.CL2020

Generating Natural Language Attacks in a Hard Label Black Box Setting

Rishabh Maheshwary, Saket Maheshwary, Vikram Pudi

We study an important and challenging task of attacking natural language processing models in a hard label black box setting. We propose a decision-based attack strategy that craft…