Catastrophic Inheritance

Catastrophic inheritance describes the undesirable propagation of biases and limitations from initial training data to downstream applications of large models, hindering performance and reliability. Current research focuses on mitigating this issue in various domains, including natural language processing (through techniques like parameter-efficient fine-tuning and bias-aware regularization) and robotics (leveraging meta-models and enhanced optimization algorithms). Addressing catastrophic inheritance is crucial for building robust and trustworthy AI systems, impacting both the development of responsible AI and the safety and efficacy of applications across diverse fields.