  We propose an auditing method to identify whether a large language model
(LLM) encodes patterns such as hallucinations in its internal states, which may
propagate to downstream tasks. We introduce a weakly supervised auditing
technique using a subset scanning approach to detect anomalous patterns in LLM
activations from pre-trained models. Importantly, our method does not need
knowledge of the type of patterns a-priori. Instead, it relies on a reference
dataset devoid of anomalies during testing. Further, our approach enables the
identification of pivotal nodes responsible for encoding these patterns, which
may offer crucial insights for fine-tuning specific sub-networks for bias
mitigation. We introduce two new scanning methods to handle LLM activations for
anomalous sentences that may deviate from the expected distribution in either
direction. Our results confirm prior findings of BERT's limited internal
capacity for encoding hallucinations, while OPT appears capable of encoding
hallucination information internally. Importantly, our scanning approach,
without prior exposure to false statements, performs comparably to a fully
supervised out-of-distribution classifier.
