  Modern language models, while sophisticated, exhibit some inherent
shortcomings, particularly in conversational settings. We claim that many of
the observed shortcomings can be attributed to violation of one or more
conversational principles. By drawing upon extensive research from both the
social science and AI communities, we propose a set of maxims -- quantity,
quality, relevance, manner, benevolence, and transparency -- for describing
effective human-AI conversation. We first justify the applicability of the
first four maxims (from Grice) in the context of human-AI interactions. We then
argue that two new maxims, benevolence (concerning the generation of, and
engagement with, harmful content) and transparency (concerning recognition of
one's knowledge boundaries, operational constraints, and intents), are
necessary for addressing behavior unique to modern human-AI interactions. We
evaluate the degree to which various language models are able to understand
these maxims and find that models possess an internal prioritization of
principles that can significantly impact their ability to interpret the maxims
accurately.
