Natural Language Mechanisms via Self-Resolution with Foundation Models
<|reference_start|>Natural Language Mechanisms via Self-Resolution with Foundation Models: 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.<|reference_end|>
@article{della penna2024natural,
    title={Natural Language Mechanisms via Self-Resolution with Foundation Models},
    author={Nicolas Della Penna},
    journal={arXiv preprint arXiv:2407.07845},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.07845},
    primaryClass={cs.GT}
}