123 citations · 333 across the 36 of their papers we have counts for
38 papers
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…
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…
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…
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…
SEPSIS: I Can Catch Your Lies -- A New Paradigm for Deception Detection
Anku Rani, Dwip Dalal, Shreya Gautam +5
Deception is the intentional practice of twisting information. It is a nuanced societal practice deeply intertwined with human societal evolution, characterized by a multitude of f…