English(EN) Signals, Evidence, and Context: User Scrutiny in Real-World LLM Conversations

新研究揭示大语言模型对话缺乏可检查的证据

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

一篇新的arXiv论文研究了用户在真实对话中与大语言模型（LLMs）的互动，分析了来自ShareChat和WildChat的超过489,000个响应对。研究发现，虽然约31.9%的响应中出现了归因语言，但明确的URL使用率仅为1.33%，这表明可检查的支持性证据存在显著差距。研究表明，归因和不确定性线索等特征伴随着用户对大语言模型响应的检查和质疑，其中不确定性在检查方面略占优势。 AI

影响强调了大语言模型在改进证据呈现方面存在关键需求，以支持用户验证和信任。

排序理由研究论文发表在arXiv上。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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Signal score: 11 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 研究论文发表在arXiv上。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Ahana Biswas, Yu-Ru Lin · 2026-11-06 05:00

信号、证据和上下文：真实世界大语言模型对话中的用户审视

arXiv:2611.08232v1 Announce Type: new Abstract: Large language models (LLMs) often present synthesized answers without making their supporting evidence easy to inspect. We study how features of these answers relate to users' subsequent accepting, checking, and challenging in 489,…

本文如何被排名

报道来源 [1]

信号、证据和上下文：真实世界大语言模型对话中的用户审视

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