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Found 4 papers, 1 papers with code
Safe and Responsible Large Language Model Development
This paper introduces a Safety and Responsible Large Language Model (\textbf{SR}$_{\text{LLM}}$ ), an approach designed to enhance the safety of LLM-generated content.
FAIR Enough: How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training?
The rapid evolution of Large Language Models (LLMs) highlights the necessity for ethical considerations and data integrity in AI development, particularly emphasizing the role of FAIR (Findable, Accessible, Interoperable, Reusable) data principles.
Analyzing the Impact of Fake News on the Anticipated Outcome of the 2024 Election Ahead of Time
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 political speeches.
Unlocking Bias Detection: Leveraging Transformer-Based Models for Content Analysis
Bias detection in text is crucial for combating the spread of negative stereotypes, misinformation, and biased decision-making.
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