123 citations · 275 across the 18 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…