  Large Language Models (LLMs) bring transformative benefits alongside unique
challenges, including intellectual property (IP) and ethical concerns. This
position paper explores a novel angle to mitigate these risks, drawing
parallels between LLMs and established web systems. We identify "citation" -
the acknowledgement or reference to a source or evidence - as a crucial yet
missing component in LLMs. Incorporating citation could enhance content
transparency and verifiability, thereby confronting the IP and ethical issues
in the deployment of LLMs. We further propose that a comprehensive citation
mechanism for LLMs should account for both non-parametric and parametric
content. Despite the complexity of implementing such a citation mechanism,
along with the potential pitfalls, we advocate for its development. Building on
this foundation, we outline several research problems in this area, aiming to
guide future explorations towards building more responsible and accountable
LLMs.
