The Homogenization Problem in LLMs: Towards Meaningful Diversity in AI Safety
A new academic paper argues that homogenization in Large Language Models (LLMs) is a critical AI safety concern. The study highlights how generative AI reproduces and amplifies human biases from training data through mechanisms like mode collapse, leading to a loss of diversity that harms marginalized groups and impoverishes society overall. To address this, the authors introduce a framework allowing stakeholders to encode their specific contexts and value systems to better characterize homogenization. The research includes an experiment using Claude 3.5 Haiku to demonstrate gender bias in open-ended storytelling. Drawing on queer and feminist theories, the paper formalizes homogenization as normativity and proposes 'xeno-reproduction' as a method to mitigate these effects by promoting diversity. This work aims to establish a collaborative research direction focused on understanding and advancing diversity within artificial intelligence systems, positioning the reduction of algorithmic homogenization as central to future AI safety protocols.
Wire timeline
The Homogenization Problem in LLMs: Towards Meaningful Diversity in AI Safety
A new academic paper argues that homogenization in Large Language Models (LLMs) is a critical AI safety concern. The study highlights how generative AI reproduces and amplifies human biases from training data through mechanisms like mode collapse, leading to a loss of diversity that harms marginalized groups and impoverishes society overall. To address this, the authors introduce a framework allowing stakeholders to encode their specific contexts and value systems to better characterize homogenization. The research includes an experiment using Claude 3.5 Haiku to demonstrate gender bias in open-ended storytelling. Drawing on queer and feminist theories, the paper formalizes homogenization as normativity and proposes 'xeno-reproduction' as a method to mitigate these effects by promoting diversity. This work aims to establish a collaborative research direction focused on understanding and advancing diversity within artificial intelligence systems, positioning the reduction of algorithmic homogenization as central to future AI safety protocols.
cs.AI updates on arXiv.org