Hostile Interpolation (tomstafford.substack.com)

A recent essay by Hollis Robbins highlights the frustrating linguistic tendency of large language models (LLMs) to utilize constructions like “not X, but Y,” which reflect their limitations in representing antonyms. This phenomenon, dubbed "hostile interpolation," underscores a significant issue in human-LLM interactions: LLMs inadvertently position their users as uninformed, thus imposing a cognitive load as readers strive to untangle or dismiss erroneous assertions. Robbins connects this behavior to broader societal communication styles, particularly in marketing and social media, where speakers use such tactics to elevate their own status while belittling the audience. The implications for the AI/ML community are profound, as this tendency in LLMs may lead to misunderstandings and reinforce negative self-perceptions among users. By framing interactions using hostile interpolation, LLMs inadvertently create a divide between their generated outputs and user comprehension. As AI technologies increasingly penetrate communication platforms, recognizing and addressing these patterns will be crucial to enhancing the effectiveness of LLMs and ensuring that they support rather than undermine user engagement. Understanding these dynamics may pave the way for more nuanced training techniques and conversational models that foster genuine understanding and respect between AI and its human counterparts.