  Retrieval augmented generation (RAG) is frequently used to mitigate
hallucinations and provide up-to-date knowledge for large language models
(LLMs). However, given that document retrieval is an imprecise task and
sometimes results in erroneous or even harmful content being presented in
context, this raises the question of how LLMs handle retrieved information: If
the provided content is incorrect, does the model know to ignore it, or does it
recapitulate the error? Conversely, when the model's initial response is
incorrect, does it always know to use the retrieved information to correct
itself, or does it insist on its wrong prior response? To answer this, we
curate a dataset of over 1200 questions across six domains (e.g., drug dosages,
Olympic records, locations) along with content relevant to answering each
question. We further apply precise perturbations to the answers in the content
that range from subtle to blatant errors. We benchmark six top-performing LLMs,
including GPT-4o, on this dataset and find that LLMs are susceptible to
adopting incorrect retrieved content, overriding their own correct prior
knowledge over 60% of the time. However, the more unrealistic the retrieved
content is (i.e. more deviated from truth), the less likely the model is to
adopt it. Also, the less confident a model is in its initial response (via
measuring token probabilities), the more likely it is to adopt the information
in the retrieved content. We exploit this finding and demonstrate simple
methods for improving model accuracy where there is conflicting retrieved
content. Our results highlight a difficult task and benchmark for LLMs --
namely, their ability to correctly discern when it is wrong in light of correct
retrieved content and to reject cases when the provided content is incorrect.
