  Practical mechanisms often limit agent reports to constrained formats like
trades or orderings, potentially limiting the information agents can express.
We propose a novel class of mechanisms that elicit agent reports in natural
language and leverage the world-modeling capabilities of large language models
(LLMs) to select outcomes and assign payoffs. We identify sufficient conditions
for these mechanisms to be incentive-compatible and efficient as the LLM being
a good enough world model and a strong inter-agent information
over-determination condition. We show situations where these LM-based
mechanisms can successfully aggregate information in signal structures on which
prediction markets fail.
