123 citations · 275 across the 18 of their papers we have counts for
18 papers
Findings of the Counter Turing Test: AI-Generated Image Detection
Rajarshi Roy, Nasrin Imanpour, Ashhar Aziz +16
The rapid advancements in generative AI technologies, such as Stable Diffusion, DALL-E, and Midjourney, have significantly transformed the creation of synthetic visual content. Whi…
Findings of the Counter Turing Test: AI-Generated Text Detection
Rajarshi Roy, Gurpreet Singh, Ashhar Aziz +16
The growing capability of large language models to produce fluent, contextually coherent text has created mounting pressure on the systems and institutions responsible for ensuring…
Trust but Verify: Introducing DAVinCI -- A Framework for Dual Attribution and Verification in Claim Inference for Language Models
Vipula Rawte, Ryan Rossi, Franck Dernoncourt +1
Large Language Models (LLMs) have demonstrated remarkable fluency and versatility across a wide range of NLP tasks, yet they remain prone to factual inaccuracies and hallucinations…
A Comprehensive Dataset for Human vs. AI Generated Image Detection
Rajarshi Roy, Ashhar Aziz, Shashwat Bajpai +17
Multimodal generative AI systems like Stable Diffusion, DALL-E, and MidJourney have fundamentally changed how synthetic images are created. These tools drive innovation but also en…
A Comprehensive Dataset for Human vs. AI Generated Text Detection
Rajarshi Roy, Gurpreet Singh, Ashhar Aziz +17
The rapid advancement of large language models (LLMs) has led to increasingly human-like AI-generated text, raising concerns about content authenticity, misinformation, and trustwo…
RADIANT: Retrieval AugmenteD entIty-context AligNmenT -- Introducing RAG-ability and Entity-Context Divergence
Vipula Rawte, Rajarshi Roy, Gurpreet Singh +11
As Large Language Models (LLMs) continue to advance, Retrieval-Augmented Generation (RAG) has emerged as a vital technique to enhance factual accuracy by integrating external knowl…
Document Attribution: Examining Citation Relationships using Large Language Models
Vipula Rawte, Ryan A. Rossi, Franck Dernoncourt +1
As Large Language Models (LLMs) are increasingly applied to document-based tasks - such as document summarization, question answering, and information extraction - where user requi…
Do Voters Get the Information They Want? Understanding Authentic Voter FAQs in the US and How to Improve for Informed Electoral Participation
Vipula Rawte, Deja N Scott, Gaurav Kumar +3
Accurate information is crucial for democracy as it empowers voters to make informed decisions about their representatives and keeping them accountable. In the US, state election c…
ViBe: A Text-to-Video Benchmark for Evaluating Hallucination in Large Multimodal Models
Vipula Rawte, Sarthak Jain, Aarush Sinha +9
Recent advances in Large Multimodal Models (LMMs) have expanded their capabilities to video understanding, with Text-to-Video (T2V) models excelling in generating videos from textu…
Visual Hallucination: Definition, Quantification, and Prescriptive Remediations
Anku Rani, Vipula Rawte, Harshad Sharma +4
The troubling rise of hallucination presents perhaps the most significant impediment to the advancement of responsible AI. In recent times, considerable research has focused on det…
FACTOID: FACtual enTailment fOr hallucInation Detection
Vipula Rawte, S. M Towhidul Islam Tonmoy, Krishnav Rajbangshi +4
The widespread adoption of Large Language Models (LLMs) has facilitated numerous benefits. However, hallucination is a significant concern. In response, Retrieval Augmented Generat…
"Sorry, Come Again?" Prompting -- Enhancing Comprehension and Diminishing Hallucination with [PAUSE]-injected Optimal Paraphrasing
Vipula Rawte, S. M Towhidul Islam Tonmoy, S M Mehedi Zaman +4
Hallucination has emerged as the most vulnerable aspect of contemporary Large Language Models (LLMs). In this paper, we introduce the Sorry, Come Again (SCA) prompting, aimed to av…
A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models
S. M Towhidul Islam Tonmoy, S M Mehedi Zaman, Vinija Jain +4
As Large Language Models (LLMs) continue to advance in their ability to write human-like text, a key challenge remains around their tendency to hallucinate generating content that…
The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediations
Vipula Rawte, Swagata Chakraborty, Agnibh Pathak +5
The recent advancements in Large Language Models (LLMs) have garnered widespread acclaim for their remarkable emerging capabilities. However, the issue of hallucination has paralle…
Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness
Vipula Rawte, Prachi Priya, S. M Towhidul Islam Tonmoy +3
As Large Language Models (LLMs) have advanced, they have brought forth new challenges, with one of the prominent issues being LLM hallucination. While various mitigation techniques…
A Survey of Hallucination in Large Foundation Models
Vipula Rawte, Amit Sheth, Amitava Das
Hallucination in a foundation model (FM) refers to the generation of content that strays from factual reality or includes fabricated information. This survey paper provides an exte…
ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance
Kaushik Roy, Manas Gaur, Misagh Soltani +3
Current Virtual Mental Health Assistants (VMHAs) provide counseling and suggestive care. They refrain from patient diagnostic assistance because they lack training in safety-constr…
FETILDA: An Effective Framework For Fin-tuned Embeddings For Long Financial Text Documents
Bolun "Namir" Xia, Vipula D. Rawte, Mohammed J. Zaki +1
Unstructured data, especially text, continues to grow rapidly in various domains. In particular, in the financial sphere, there is a wealth of accumulated unstructured financial da…