Retrieval-augmented generation (RAG) mitigates many problems of fully
parametric language models, such as temporal degradation, hallucinations, and
lack of grounding. In RAG, the model's knowledge can be updated from documents
provided in context. This leads to cases of conflict between the model's
parametric knowledge and the contextual information, where the model may not
always update its knowledge. Previous work studied knowledge conflicts by
creating synthetic documents that contradict the model's correct parametric
answers. We present a framework for studying knowledge conflicts in a realistic
setup. We update incorrect parametric knowledge using real conflicting
documents. This reflects how knowledge conflicts arise in practice. In this
realistic scenario, we find that knowledge updates fail less often than
previously reported. In cases where the models still fail to update their
answers, we find a parametric bias: the incorrect parametric answer appearing
in context makes the knowledge update likelier to fail. These results suggest
that the factual parametric knowledge of LLMs can negatively influence their
reading abilities and behaviors. Our code is available at
this https URL

通过真实冲突文档更新错误的派生参数知识来研究知识冲突，结果显示模型的正确派生参数知识对其阅读能力和行为产生负面影响。