  Effective conversation requires common ground: a shared understanding between
the participants. Common ground, however, does not emerge spontaneously in
conversation. Speakers and listeners work together to both identify and
construct a shared basis while avoiding misunderstanding. To accomplish
grounding, humans rely on a range of dialogue acts, like clarification (What do
you mean?) and acknowledgment (I understand.). However, it is unclear whether
large language models (LLMs) generate text that reflects human grounding. To
this end, we curate a set of grounding acts and propose corresponding metrics
that quantify attempted grounding. We study whether LLM generations contain
grounding acts, simulating turn-taking from several dialogue datasets and
comparing results to humans. We find that -- compared to humans -- LLMs
generate language with less conversational grounding, instead generating text
that appears to simply presume common ground. To understand the roots of the
identified grounding gap, we examine the role of instruction tuning and
preference optimization, finding that training on contemporary preference data
leads to a reduction in generated grounding acts. Altogether, we highlight the
need for more research investigating conversational grounding in human-AI
interaction.
