Líkanahættu

Líkanahættu is an Icelandic term that translates to "risk of model collapse" in English. It refers to a phenomenon that can occur in machine learning, particularly in generative models like those used for creating text or images. Model collapse happens when a generative model, during its training process, starts to overfit to its own generated data. Instead of learning the underlying distribution of the real data it was initially trained on, the model begins to produce outputs that are increasingly similar to its previous outputs.

This can lead to a degradation of the model's ability to generate diverse and novel content. The generated samples become repetitive, lacking originality and failing to capture the full complexity of the original data distribution. In essence, the model gets stuck in a loop, producing variations of what it has already created, rather than exploring new possibilities. This can be a significant problem for applications that rely on generating varied and realistic outputs, such as in creative arts, synthetic data generation for training other models, or even in conversational AI where repetitive responses can quickly become apparent and frustrating.

Several factors can contribute to líkanahættu, including insufficient training data, overly simplistic model architectures, or aggressive training techniques that encourage rapid convergence. Researchers are actively developing methods to detect and mitigate model collapse, such as using improved training objectives, incorporating more diversity into the training process, or employing techniques that monitor the quality and diversity of generated samples over time. Addressing líkanahættu is crucial for building more robust and useful generative AI systems.