activity
20182025
most citedAnalyzing Semantic Faithfulness of Language Models via Input Intervention on Question Answering

3 citations · 5 across the 3 of their papers we have counts for

collaborators

8 papers

cs.CL2025

sudoLLM: On Multi-role Alignment of Language Models

Soumadeep Saha, Akshay Chaturvedi, Joy Mahapatra +1

User authorization-based access privileges are a key feature in many safety-critical systems, but have not been extensively studied in the large language model (LLM) realm. In this…

cs.CL2023★ 2 cited

Limits for Learning with Language Models

Nicholas Asher, Swarnadeep Bhar, Akshay Chaturvedi +2

With the advent of large language models (LLMs), the trend in NLP has been to train LLMs on vast amounts of data to solve diverse language understanding and generation tasks. The l…

cs.CL2022★ 3 cited

Analyzing Semantic Faithfulness of Language Models via Input Intervention on Question Answering

Akshay Chaturvedi, Swarnadeep Bhar, Soumadeep Saha +2

Transformer-based language models have been shown to be highly effective for several NLP tasks. In this paper, we consider three transformer models, BERT, RoBERTa, and XLNet, in bo…

cs.CV2020

Pick-Object-Attack: Type-Specific Adversarial Attack for Object Detection

Omid Mohamad Nezami, Akshay Chaturvedi, Mark Dras +1

Many recent studies have shown that deep neural models are vulnerable to adversarial samples: images with imperceptible perturbations, for example, can fool image classifiers. In t…

cs.LG2019

Exploring the Robustness of NMT Systems to Nonsensical Inputs

Akshay Chaturvedi, Abijith KP, Utpal Garain

Neural machine translation (NMT) systems have been shown to give undesirable translation when a small change is made in the source sentence. In this paper, we study the behaviour o…

cs.CV2019

Mimic and Fool: A Task Agnostic Adversarial Attack

Akshay Chaturvedi, Utpal Garain

At present, adversarial attacks are designed in a task-specific fashion. However, for downstream computer vision tasks such as image captioning, image segmentation etc., the curren…