LLMs struggle with dynamic financial reasoning in board game simulations

Researchers have developed FinBoardBench, a new evaluation suite designed to test the dynamic financial reasoning and wealth management capabilities of large language models (LLMs). The suite utilizes three classic board games: Cashflow, Acquire, and Monopoly, to assess skills such as cash flow management, investment forecasting, and negotiation. Experiments with nine advanced LLMs showed that while they possess basic planning abilities, they struggle with complex interactions and dynamic decision-making, often prioritizing asset acquisition over liquidity and becoming vulnerable to financial crises. AI

IMPACT This benchmark could reveal critical limitations in LLMs' real-world financial decision-making, guiding future development towards more robust and adaptable AI agents.

RANK_REASON The cluster describes a new academic paper introducing a novel benchmark for evaluating LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

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• FinBoardBench
• LLMs
• Monopoly

• paper
• product

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