使用大模型会招致他人惩罚，越依赖越遭报复。
Antisocial behavior towards large language model users: experimental evidence
- 让参与者花钱惩罚用或不用大模型完成任务的同龄人
- 完全依赖模型者被罚掉36%收益，使用越多惩罚越重
- 声称不用反而更遭殃，真实高用量更被严惩
大语言模型（LLMs）的快速普及引发了对其社会反应的关注。以往研究记录了对AI使用者的负面态度，但尚不清楚这种不满是否转化为实际成本行为。我们通过两阶段在线实验（第二阶段参与人数为491人；第一阶段提供目标对象）探讨此问题，参与者可花费自己部分资金来减少曾完成真实努力任务的同龄人的收益，这些任务是否借助大模型支持。结果显示，平均而言，完全依赖模型的个体被剥夺了36%的收益，且惩罚程度随实际模型使用量单调递增。关于模型使用的声明引发可信度差距：声称未使用却实际未用者受到更严厉惩罚，表明“未使用”声明被怀疑；而高使用率情况下，实际使用比声称使用遭受更严重惩罚。这些发现首次提供了行为证据，表明大模型带来的效率提升伴随着社会制裁代价。
原文摘要 · Abstract (English)
The rapid spread of large language models (LLMs) has raised concerns about the social reactions they provoke. Prior research documents negative attitudes toward AI users, but it remains unclear whether such disapproval translates into costly action. We address this question in a two-phase online experiment (N = 491 Phase II participants; Phase I provided targets) where participants could spend part of their own endowment to reduce the earnings of peers who had previously completed a real-effort task with or without LLM support. On average, participants destroyed 36% of the earnings of those who relied exclusively on the model, with punishment increasing monotonically with actual LLM use. Disclosure about LLM use created a credibility gap: self-reported null use was punished more harshly than actual null use, suggesting that declarations of "no use" are treated with suspicion. Conversely, at high levels of use, actual reliance on the model was punished more strongly than self-reported reliance. Taken together, these findings provide the first behavioral evidence that the efficiency gains of LLMs come at the cost of social sanctions.
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