3 papers - avg viability 7.0
A fine-tuning approach to align large language models with biological solutions, enhancing AI safety.
A unified framework to analyze and mitigate gender bias in LLMs by correlating internal representations with expressed outputs, demonstrating that current debiasing methods don't fully remove latent bias.
This research develops and evaluates multi-agent architectures and prompt engineering techniques to mitigate dialect-based linguistic stereotypes in LLM outputs, offering a path towards fairer AI deployments.