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The GAIN benchmark is introduced to evaluate LLMs' ability to balance business goals with adherence to norms in realistic scenarios, using pressures designed to encourage norm deviations. It includes 1,200 scenarios across hiring, customer support, advertising, and finance, with pressures like Goal Alignment, Risk Aversion, and Personal Incentive. Experiments reveal that while LLMs generally mirror human decision-making, they exhibit a heightened tendency to adhere to norms when Personal Incentive pressures are present.
LLMs surprisingly prioritize norm adherence over personal incentives in business scenarios, challenging assumptions about goal-driven behavior.
We introduce GAIN (Goal-Aligned Decision-Making under Imperfect Norms), a benchmark designed to evaluate how large language models (LLMs) balance adherence to norms against business goals. Existing benchmarks typically focus on abstract scenarios rather than real-world business applications. Furthermore, they provide limited insights into the factors influencing LLM decision-making. This restricts their ability to measure models'adaptability to complex, real-world norm-goal conflicts. In GAIN, models receive a goal, a specific situation, a norm, and additional contextual pressures. These pressures, explicitly designed to encourage potential norm deviations, are a unique feature that differentiates GAIN from other benchmarks, enabling a systematic evaluation of the factors influencing decision-making. We define five types of pressures: Goal Alignment, Risk Aversion, Emotional/Ethical Appeal, Social/Authoritative Influence, and Personal Incentive. The benchmark comprises 1,200 scenarios across four domains: hiring, customer support, advertising and finance. Our experiments show that advanced LLMs frequently mirror human decision-making patterns. However, when Personal Incentive pressure is present, they diverge significantly, showing a strong tendency to adhere to norms rather than deviate from them.
