  This survey provides an in-depth analysis of knowledge conflicts for large
language models (LLMs), highlighting the complex challenges they encounter when
blending contextual and parametric knowledge. Our focus is on three categories
of knowledge conflicts: context-memory, inter-context, and intra-memory
conflict. These conflicts can significantly impact the trustworthiness and
performance of LLMs, especially in real-world applications where noise and
misinformation are common. By categorizing these conflicts, exploring the
causes, examining the behaviors of LLMs under such conflicts, and reviewing
available solutions, this survey aims to shed light on strategies for improving
the robustness of LLMs, thereby serving as a valuable resource for advancing
research in this evolving area.
