Alignment Forecasting: Predicting Misalignment From Training Data
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-hoc audits, we introduce Alignment Forecasting: the task of predicting alignment failures before training. Given a target model, a fine-tuning dataset, and a failure mode such as deception or sycophancy, a forecaster outputs the probability that fine-tuning would meaningfully increase that failure mode. To measure progress on alignment forecasting, we introduce ALIGNMENTFORECASTBENCH, a benchmark of over 5,000 forec...
| Authors | Chen Yueh-Han, Bruce W. Lee, Ilia Sucholutsky et al. |
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| Primary category | cs.CL |
| Published | 19 September 2026 |
| Last updated at source | 30 September 2026 |
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