Scaling Implicit Bias Analysis across Transformer-Based Language Models through Embedding Association Test and Prompt Engineering

In the evolving field of machine learning, deploying fair and transparent models remains a formidable challenge. This study builds on earlier research, demonstrating that neural architectures exhibit inherent biases by analyzing a broad spectrum of transformer-based language models from base to x-la...

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Autors principals:	Ravi Varma Kumar Bevara, Nishith Reddy Mannuru, Sai Pranathi Karedla, Ting Xiao
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2024-04-01
Col·lecció:	Applied Sciences
Matèries:	AI model scaling Generative Pretrained Transformer (GPT) k-means Bidirectional Encoder Representations from Transformers (BERT) transformer
Accés en línia:	https://www.mdpi.com/2076-3417/14/8/3483
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Scaling Implicit Bias Analysis across Transformer-Based Language Models through Embedding Association Test and Prompt Engineering

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