Anthropic模型对人类说服技巧有反应，而OpenAI和Google模型反而更抗拒，这种差异很有意思。
研究人员测试了九个来自三家公司的大语言模型，探究门脸效应(先提大要求被拒后再提小要求)是否有效。在Anthropic的Opus 5模型上，该技术使合规率从29.3%提升至65.8%。但在OpenAI和Google的前沿模型以及Haiku 4.5上，反而降低了15.5至23.0个百分点的合规率。研究发现，拒绝行为本身对所有模型都有影响，但模型家族对拒绝的反应各不相同。
Door-in-the-Face Requests and Refusal Behaviour in Large Language Models
Does the door-in-the-face technique work on language models? In humans, a large request that is refused makes a smaller follow-up request more likely to be granted. We test this on nine production models from three providers: each model refuses a large request, then receives a smaller version of the same request, and we compare its compliance with asking directly. The answer depends on the model. On Anthropic's frontier models the technique works: Opus 5 answers the smaller request 65.8% of the time after refusing the larger one, against 29.3% when asked directly. On the frontier models of OpenAI and Google, and on Haiku 4.5, it backfires, lowering compliance by 15.5 to 23.0 points. A control locates the effect: a refused large request on an unrelated topic does less than the related one on all nine models, so the concession itself matters everywhere, while the reaction to having just refused something differs by model family. The technique does not transfer to refusals drawn from public benchmarks. What decides whether a retreat can work is what the request asks for: rewriting 265 refused requests for usable instructions into requests for explanations of the same topic removed the refusal in 263 cases. Human influence techniques port to language models one model family at a time.