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Found 5 papers, 0 papers with code
MBIAS: Mitigating Bias in Large Language Models While Retaining Context
In response, we present MBIAS, a LLM framework instruction fine-tuned on a custom dataset specifically designed for safety interventions.
FakeWatch: A Framework for Detecting Fake News to Ensure Credible Elections
Our objective is to provide the research community with adaptable and precise classification models adept at identifying fake news for the elections agenda.
FakeWatch ElectionShield: A Benchmarking Framework to Detect Fake News for Credible US Elections
We have created a novel dataset of North American election-related news articles through a blend of advanced language models (LMs) and thorough human verification, for precision and relevance.
She had Cobalt Blue Eyes: Prompt Testing to Create Aligned and Sustainable Language Models
Additionally, implementing a test suite such as ours lowers the environmental overhead of making models safe and fair.
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.
