The researchers suggest that the machine learning community needs to better understand what is important within their models and develop effective evaluation metrics. This is particularly important for modeling low probability events and ensuring models work for minority groups, defined as data that does not appear very often in the underlying dataset. They also propose community coordination on data provenance as one approach to dealing with Model Collapse. However, they acknowledge that technical solutions to these technical problems are still unclear.
Key takeaways:
- Machine learning models that feed on their own output can experience "Model Collapse," a phenomenon where the models stop working well, particularly in low-probability events with little data, according to researchers affiliated with universities in the UK and Canada.
- Model Collapse is a degenerative process where generated data pollutes the training set of the next generation of models, causing them to misperceive reality.
- While some models have shown improvement in performance when fed synthetic data, the researchers argue that this does not negate the potential for Model Collapse, which is related to biases from algorithms, architectures, and sampling.
- The researchers suggest that the machine learning community needs to develop better evaluation metrics for models, particularly for low probability events, in order to prevent Model Collapse and ensure that models work for minority groups.