1 citations · 2 across the 7 of their papers we have counts for
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Recontextualization Mitigates Specification Gaming without Modifying the Specification
Ariana Azarbal, Victor Gillioz, Vladimir Ivanov +6
Developers often struggle to specify correct training labels and rewards. Perhaps they don't need to. We propose recontextualization, which reduces how often language models "game"…
Inoculation Prompting: Instructing LLMs to misbehave at train-time improves test-time alignment
Nevan Wichers, Aram Ebtekar, Ariana Azarbal +8
Large language models are sometimes trained with imperfect oversight signals, leading to undesired behaviors such as reward hacking and sycophancy. Improving oversight quality can…
Toward Universal Laws of Outlier Propagation
Aram Ebtekar, Yuhao Wang, Dominik Janzing
When a variety of anomalous features motivate flagging different samples as outliers, Algorithmic Information Theory (AIT) offers a principled way to unify them in terms of a sampl…