LLM 风险行为基准测试：新模型更稳定且更风险中性
Benchmarking AI risk behaviors
AI 导读
arXiv 论文对 LLM 在多种情境与任务需求下的风险倾向进行了基准评估，测量模型的风险规避或风险容忍程度及其跨条件的一致性，并提供了可扩展的模型风险画像工具。研究发现 2026 年 SOTA 模型比旧模型更稳定、更一致且更接近风险中性，原因是它们更倾向采用期望值最大化作为决策程序。
正文 · 原文
Abstract:We evaluate LLM risk propensities across a range of contexts and task demands. We evaluate models for how risk averse or risk tolerant they are in various conditions, as well as how stable and consistent their behavior is across conditions. We present a toolkit for developing model risk profiles that can be used to predict model behavior and which can be adapted to include new evaluations of risk propensities. We also examine how these properties have changed across model generations and families. We observe that 2026 state-of-the-art models tend to be more stable, more consistent, and more risk neutral than older models. These trends appear to be related; newer models are more stable and consistent because they are more likely to use expected value maximization as a decision procedure.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.16318 [cs.AI] |
| (or arXiv:2611.16318v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.16318 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Luhan Mikaelson [view email]
[v1]
Thu, 12 Nov 2026 05:27:44 UTC (1,562 KB)
来源：arXiv cs.AI · arxiv.org