English(EN) Communication styles and reader preferences of LLM- and human-authored COVID-19 information explanations: a case study

LLM撰写的COVID-19信息因清晰度而受青睐，尽管质量较低

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-28 04:00

一项发表在arXiv上的新研究调查了大型语言模型（LLM）和人类在生成COVID-19信息方面的沟通风格。研究人员发现，尽管LLM生成的内容在说服策略和社会价值观契合度方面得分较低，但读者压倒性地偏爱其清晰度、完整性和中立的语气。这种偏好表明，读者可能将结构化和清晰的呈现方式与专业性联系起来，即使它缺乏细致的沟通质量。 AI

影响读者在健康传播中偏爱LLM内容，凸显了在清晰度与既定质量指标之间取得平衡的必要性。

排序理由关于LLM沟通风格和读者偏好的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 25 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 关于LLM沟通风格和读者偏好的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Jiawei Zhou, Kritika Venkatachalam, Minje Choi, Koustuv Saha, Munmun De Choudhury · 2026-08-28 04:00

LLM与人类撰写的COVID-19信息解释的沟通风格和读者偏好：一项案例研究

arXiv:2505.08143v2 Announce Type: replace-cross Abstract: With the wide adoption of large language models (LLMs) in information assistance, it is essential to examine their alignment with human communication styles and values. We situate this study within health fact-checking, wh…

本文如何被排名

报道来源 [1]

LLM与人类撰写的COVID-19信息解释的沟通风格和读者偏好：一项案例研究

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