123 citations · 147 across the 56 of their papers we have counts for
9 papers · 2 filters
LLMsAgainstHate @ NLU of Devanagari Script Languages 2025: Hate Speech Detection and Target Identification in Devanagari Languages via Parameter Efficient Fine-Tuning of LLMs
Rushendra Sidibomma, Pransh Patwa, Parth Patwa +3
The detection of hate speech has become increasingly important in combating online hostility and its real-world consequences. Despite recent advancements, there is limited research…
KnowledgePrompts: Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting
Thilini Wijesiriwardene, Ruwan Wickramarachchi, Sreeram Vennam +5
Making analogies is fundamental to cognition. Proportional analogies, which consist of four terms, are often used to assess linguistic and cognitive abilities. For instance, comple…
Overview of Factify5WQA: Fact Verification through 5W Question-Answering
Suryavardan Suresh, Anku Rani, Parth Patwa +6
Researchers have found that fake news spreads much times faster than real news. This is a major problem, especially in today's world where social media is the key source of news fo…
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…
Counter Turing Test (): Investigating AI-Generated Text Detection for Hindi -- Ranking LLMs based on Hindi AI Detectability Index ()
Ishan Kavathekar, Anku Rani, Ashmit Chamoli +3
The widespread adoption of Large Language Models (LLMs) and awareness around multilingual LLMs have raised concerns regarding the potential risks and repercussions linked to the mi…
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…