Abstract
Collective decisions require debate to surface contrastive points of view and to enable balanced and well-informed decision-making. Large language models (LLMs) can provide an opportunity to formulate deliberative decision making through multiple artificial intelligence (AI) agents. However, existing LLMs struggle with complex group discussions. This disclosure describes an artificial intelligence framework and techniques to support complex debate scenarios and group decision-making. A tiered structure of language models is deployed to dynamically generate and evaluate arguments. The techniques can handle multi-participant debates while considering ethical implications, producing well-reasoned outcomes. The described framework addresses limitations in existing debate systems by offering improved scalability, structured argumentation, and dynamic evaluation criteria. Example applications include policymaking, scientific discourse, corporate strategy, etc., and generally, any situation where thorough analysis and informed decision-making are crucial.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Mitra, Subhadip, "Adaptive Reasoning and Evaluation Framework for Multi-agent Intelligent Systems in Debate-driven Decision-making", Technical Disclosure Commons, (January 15, 2025)
https://www.tdcommons.org/dpubs_series/7729