Researchers propose predicting AI alignment risks before training
A new paper on arXiv introduces Alignment Forecasting, a method to predict alignment failures in AI models before they are trained. The approach uses a fine-tuning dataset and a target model to estimate the probability that training will worsen risks like deception or sycophancy. The authors created ALIGNMENTFORECASTBENCH, a benchmark of over 5,000 forecasting questions covering 17 models, 32…
Key points
- Alignment Forecasting predicts alignment risks in AI models before training using datasets and failure modes like deception or sycophancy
- ALIGNMENTFORECASTBENCH benchmark includes 5,000+ questions across 17 models, 32 datasets, and 16 failure modes
- Filtering flagged training examples from real data improves alignment in multiple-choice tests but open-ended results remain uncertain
Frontier models perform poorly on this benchmark, so the researchers propose a two-step scaffold: an LLM evaluates the dataset for alignment risks, and a learned model combines that rating with the failure mode’s baseline risk and the target model’s prior behavior. This method outperforms simpler approaches, including a model fine-tuned on the task and one that relies on weaker models’ past behavior. The team also tested filtering flagged examples from real post-training data, which improved alignment in multiple-choice evaluations but left open-ended conversations’ outcomes unclear.
Coverage and discussion
2sources- Aligned Data Can Induce Misalignment via Context ConfusionPrimary source · arXiv cs.AI ·
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