arXiv:2504.17083cs.CL2025-04中稿 · GenAICHI 2025 @ AC…被引 10

语言风格比准确度更影响用户对LLM的偏好,且因人而异。

How Individual Traits and Language Styles Shape Preferences In Open-ended User-LLM Interaction: A Preliminary Study

  • 通过用户实验发现,LLM回复的语言风格显著影响用户偏好。
  • 不同用户群体对语言风格的偏好差异明显,受个体特质调节。
  • 适合关注人机交互体验、个性化AI设计的研究者阅读。

什么让与LLM的互动更受用户青睐?尽管信息准确性看似关键,但研究表明,即使回复不准确,只要语气权威、确定、表达流畅或篇幅较长,用户仍可能更偏好。这些因素均属语言风格范畴,暗示语言风格可能深刻影响用户偏好。这一机制具有双面性:既能提升体验,也可能增加用户对虚假信息的易感性。本文通过一系列探索性与实验性用户研究发现,语言风格确实影响偏好,但具体影响方式因用户群体而异,且受用户自身个体特质调节。作为初步研究,结果需谨慎解读,因样本在人口多样性与规模上仍有局限。未来工作将致力于扩大样本,开展语言风格、个体特质与偏好的联合效应分析,并探究其潜在因果关系。

原文摘要 · Abstract (English)

What makes an interaction with the LLM more preferable for the user? While it is intuitive to assume that information accuracy in the LLM's responses would be one of the influential variables, recent studies have found that inaccurate LLM's responses could still be preferable when they are perceived to be more authoritative, certain, well-articulated, or simply verbose. These variables interestingly fall under the broader category of language style, implying that the style in the LLM's responses might meaningfully influence users' preferences. This hypothesized dynamic could have double-edged consequences: enhancing the overall user experience while simultaneously increasing their susceptibility to risks such as LLM's misinformation or hallucinations. In this short paper, we present our preliminary studies in exploring this subject. Through a series of exploratory and experimental user studies, we found that LLM's language style does indeed influence user's preferences, but how and which language styles influence the preference varied across different user populations, and more interestingly, moderated by the user's very own individual traits. As a preliminary work, the findings in our studies should be interpreted with caution, particularly given the limitations in our samples, which still need wider demographic diversity and larger sample sizes. Our future directions will first aim to address these limitations, which would enable a more comprehensive joint effect analysis between the language style, individual traits, and preferences, and further investigate the potential causal relationship between and beyond these variables.

人机交互语言风格用户偏好

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