GAIN: A Benchmark for Goal-Aligned Decision-Making of Large Language Models under Imperfect Norms
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 diff...
| Authors | Masayuki Kawarada, Kodai Watanabe, Soichiro Murakami |
|---|---|
| Primary category | cs.CL |
| Published | 19 March 2026 |
| Last updated at source | 19 March 2026 |
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