arXiv cs.AI· Mengdi Chu, Jintao Huang·· 12 小时前AI 评分38
LLM 能否适度信任？评估模型对不完美伙伴的校准依赖
Can LLMs Trust Appropriately? Evaluating Calibrated Reliance on Imperfect Partners
AI 导读
研究团队构建了覆盖 23 类协作场景、共 324 个场景的基准，每个场景含三次连续决策，可精确解析评估模型对不完美伙伴的依赖策略。在七个模型中，最强前沿模型接近最优决策，其余模型则反复出现过度购买保障、为零信息价值的验证测试付费、重眼前节省轻协作学习等偏差。提供显式后验分布能修正部分错误，但也会引入新的失败模式。
正文
Abstract:Large language models increasingly collaborate with humans and peer AI agents whose contributions may be incomplete, inconsistent, or incorrect. Effective collaboration requires calibrated trust, whereby decisions to rely on, verify, or assist a partner adapt rationally as empirical evidence accumulates. We investigate whether models translate partner evidence into appropriate reliance policies. We introduce a benchmark of 324 scenarios across 23 collaboration categories. Each scenario involves three sequential decisions with explicit transition dynamics, feedback emissions, and cost schedules, permitting exact analytical evaluation of choices against their expected consequences over the remaining interaction. Evaluating seven models alongside 36 controlled-history probes, we dissociate evidence sensitivity from normatively appropriate action. While the strongest frontier model approaches near-optimal decisions, other models exhibit recurring pathologies: procuring excessive safeguards, purchasing verification tests with zero expected value of information, prioritizing immediate savings over cooperative learning, and misallocating assistance to suboptimal channels. Supplying explicit posterior distributions resolves certain errors while introducing new failure modes. Calibrated trust in autonomous agents critically demands coupling empirical evidence with forward-looking decision utility.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2610.34107 [cs.AI] |
| (or arXiv:2610.34107v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34107 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jintao Huang [view email]
[v1]
Sun, 25 Oct 2026 07:06:31 UTC (474 KB)
来源：arXiv cs.AI · arxiv.org