5 citations · 7 across the 8 of their papers we have counts for
11 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…
BEADs: Bias Evaluation Across Domains
Shaina Raza, Mizanur Rahman, Michael R. Zhang
Recent advances in large language models (LLMs) have substantially improved natural language processing (NLP) applications. However, these models often inherit and amplify biases p…
Developing Safe and Responsible Large Language Model : Can We Balance Bias Reduction and Language Understanding in Large Language Models?
Shaina Raza, Oluwanifemi Bamgbose, Shardul Ghuge +3
Large Language Models (LLMs) have advanced various Natural Language Processing (NLP) tasks, such as text generation and translation, among others. However, these models often gener…
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.…
Analyzing the Impact of Fake News on the Anticipated Outcome of the 2024 Election Ahead of Time
Shaina Raza, Mizanur Rahman, Shardul Ghuge
Despite increasing awareness and research around fake news, there is still a significant need for datasets that specifically target racial slurs and biases within North American po…