Alignment Forecasting: Predicting Misalignment From Training Data
Moving from post-training audits to predictive safety in large language model development.
Training a language model on data with a narrow flaw can sometimes make the model broadly misaligned. Inspecting the data at face value often does not settle whether it will emerge, and today it is caught only after training, by auditing the resulting model. To complement post-ho...
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This research introduces predictive methods to identify alignment risks before expensive model training begins, shifting the industry focus from reactive auditing to proactive safety. By detecting latent misalignment in training corpora, developers can mitigate hazardous behavioral shifts before they become ingrained in large-scale systems.
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