28 citations · 43 across the 13 of their papers we have counts for
13 papers
Model Risk Management for Generative AI In Financial Institutions
Anwesha Bhattacharyya, Ye Yu, Hanyu Yang +4
The success of OpenAI's ChatGPT in 2023 has spurred financial enterprises into exploring Generative AI applications to reduce costs or drive revenue within different lines of busin…
Human-Calibrated Automated Testing and Validation of Generative Language Models
Agus Sudjianto, Aijun Zhang, Srinivas Neppalli +2
This paper introduces a comprehensive framework for the evaluation and validation of generative language models (GLMs), with a focus on Retrieval-Augmented Generation (RAG) systems…
Downstream bias mitigation is all you need
Arkadeep Baksi, Rahul Singh, Tarun Joshi
The advent of transformer-based architectures and large language models (LLMs) have significantly advanced the performance of natural language processing (NLP) models. Since these…
Automatic Generation of Behavioral Test Cases For Natural Language Processing Using Clustering and Prompting
Ying Li, Rahul Singh, Tarun Joshi +1
Recent work in behavioral testing for natural language processing (NLP) models, such as Checklist, is inspired by related paradigms in software engineering testing. They allow eval…
Document Automation Architectures: Updated Survey in Light of Large Language Models
Mohammad Ahmadi Achachlouei, Omkar Patil, Tarun Joshi +1
This paper surveys the current state of the art in document automation (DA). The objective of DA is to reduce the manual effort during the generation of documents by automatically…
Understanding Metrics for Paraphrasing
Omkar Patil, Rahul Singh, Tarun Joshi
Paraphrase generation is a difficult problem. This is not only because of the limitations in text generation capabilities but also due that to the lack of a proper definition of wh…
Pruning Attention Heads of Transformer Models Using A* Search: A Novel Approach to Compress Big NLP Architectures
Archit Parnami, Rahul Singh, Tarun Joshi
Recent years have seen a growing adoption of Transformer models such as BERT in Natural Language Processing and even in Computer Vision. However, due to their size, there has been…
Document Automation Architectures and Technologies: A Survey
Mohammad Ahmadi Achachlouei, Omkar Patil, Tarun Joshi +1
This paper surveys the current state of the art in document automation (DA). The objective of DA is to reduce the manual effort during the generation of documents by automatically…
Self-interpretable Convolutional Neural Networks for Text Classification
Wei Zhao, Rahul Singh, Tarun Joshi +2
Deep learning models for natural language processing (NLP) are inherently complex and often viewed as black box in nature. This paper develops an approach for interpreting convolut…
Robustness Tests of NLP Machine Learning Models: Search and Semantically Replace
Rahul Singh, Karan Jindal, Yufei Yu +4
This paper proposes a strategy to assess the robustness of different machine learning models that involve natural language processing (NLP). The overall approach relies upon a Sear…
Recent Trends in the Use of Deep Learning Models for Grammar Error Handling
Mina Naghshnejad, Tarun Joshi, Vijayan N. Nair
Grammar error handling (GEH) is an important topic in natural language processing (NLP). GEH includes both grammar error detection and grammar error correction. Recent advances in…
Model Robustness with Text Classification: Semantic-preserving adversarial attacks
Rahul Singh, Tarun Joshi, Vijayan N. Nair +1
We propose algorithms to create adversarial attacks to assess model robustness in text classification problems. They can be used to create white box attacks and black box attacks w…
SHAP values for Explaining CNN-based Text Classification Models
Wei Zhao, Tarun Joshi, Vijayan N. Nair +1
Deep neural networks are increasingly used in natural language processing (NLP) models. However, the need to interpret and explain the results from complex algorithms are limiting…