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Alignment Forecasting: Predicting Misalignment From Training Data
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alignmentfine-tuningforecasting
2609.35805
Builder Relevance
1h ago70%
Abstract
The paper introduces Alignment Forecasting, a method to predict alignment failures in language models before training based on fine-tuning datasets and failure modes.
Reality Card
Core Claim
The proposed forecasting scaffold can predict alignment failures with better accuracy than existing models and flags problematic training examples that may lead to misalignment.
Method / Result
The forecasting model outperforms a fine-tuned model and a simple forecaster, achieving forecasts well above chance.
Limitations
More progress is needed before forecasts can reliably guide training data curation in practice.
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