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

123 citations · 164 across the 23 of their papers we have counts for

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cs.CL20242 cited

Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs

Ronit Singhal, Pransh Patwa, Parth Patwa +2

Given the widespread dissemination of misinformation on social media, implementing fact-checking mechanisms for online claims is essential. Manually verifying every claim is very c…

cs.CL2024

MemeGuard: An LLM and VLM-based Framework for Advancing Content Moderation via Meme Intervention

Prince Jha, Raghav Jain, Konika Mandal +3

In the digital world, memes present a unique challenge for content moderation due to their potential to spread harmful content. Although detection methods have improved, proactive…

cs.CL20241 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.CL20241 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.CL20246 cited

The What, Why, and How of Context Length Extension Techniques in Large Language Models -- A Detailed Survey

Saurav Pawar, S. M Towhidul Islam Tonmoy, S M Mehedi Zaman +3

The advent of Large Language Models (LLMs) represents a notable breakthrough in Natural Language Processing (NLP), contributing to substantial progress in both text comprehension a…

cs.CL2024123 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…