2 citations · 3 across the 15 of their papers we have counts for
6 papers · 1 filter
Fact or Fiction? Can LLMs be Reliable Annotators for Political Truths?
Veronica Chatrath, Marcelo Lotif, Shaina Raza
Political misinformation poses significant challenges to democratic processes, shaping public opinion and trust in media. Manual fact-checking methods face issues of scalability an…
MBIAS: Mitigating Bias in Large Language Models While Retaining Context
Shaina Raza, Ananya Raval, Veronica Chatrath
The deployment of Large Language Models (LLMs) in diverse applications necessitates an assurance of safety without compromising the contextual integrity of the generated content. T…
FakeWatch: A Framework for Detecting Fake News to Ensure Credible Elections
Shaina Raza, Tahniat Khan, Veronica Chatrath +3
In today's technologically driven world, the rapid spread of fake news, particularly during critical events like elections, poses a growing threat to the integrity of information.…
FakeWatch ElectionShield: A Benchmarking Framework to Detect Fake News for Credible US Elections
Tahniat Khan, Mizanur Rahman, Veronica Chatrath +2
In today's technologically driven world, the spread of fake news, particularly during crucial events such as elections, presents an increasing challenge to the integrity of informa…
She had Cobalt Blue Eyes: Prompt Testing to Create Aligned and Sustainable Language Models
Veronica Chatrath, Oluwanifemi Bamgbose, Shaina Raza
As the use of large language models (LLMs) increases within society, as does the risk of their misuse. Appropriate safeguards must be in place to ensure LLM outputs uphold the ethi…
Unlocking Bias Detection: Leveraging Transformer-Based Models for Content Analysis
Shaina Raza, Oluwanifemi Bamgbose, Veronica Chatrath +3
Bias detection in text is crucial for combating the spread of negative stereotypes, misinformation, and biased decision-making. Traditional language models frequently face challeng…