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20222026
most citedA Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models

123 citations · 275 across the 18 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.CY2024

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…

cs.CV2024

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…

cs.AI2024

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…

cs.CL2024★ 1 cited

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…

cs.CL2024★ 1 cited

"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…

cs.CL2024★ 123 cited

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…