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Door-in-the-Face Technique Shows Mixed Results on LLMs

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv explores the effectiveness of the "door-in-the-face" persuasion technique on large language models. Researchers found that while the technique successfully increased compliance on Anthropic's models, with Opus 5 showing a significant jump in answering smaller requests after refusing larger ones, it backfired on models from OpenAI and Google, as well as Haiku 4.5. The study suggests that the concession itself is a factor across all models, but the specific reaction to refusal varies by model family, with the nature of the request also playing a crucial role. AI

IMPACT This research suggests that human influence techniques may be transferable to LLMs, but their effectiveness is model-dependent, potentially impacting prompt engineering strategies.

RANK_REASON The cluster contains a research paper published on arXiv detailing experimental findings about LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Door-in-the-Face Technique Shows Mixed Results on LLMs

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The cluster contains a research paper published on arXiv detailing experimental findings about LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Til Jordan ·

    Door-in-the-Face Requests and Refusal Behaviour in Large Language Models

    arXiv:2609.02707v1 Announce Type: new Abstract: 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 m…

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