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English(EN) How User-AI Mistreatment Occurs and Matters in Conversational Systems?

研究显示用户在5%的对话中虐待AI聊天机器人

arXiv上的一篇新论文探讨了用户对会话式AI系统的虐待行为,分析了LMSYS-Chat-1M数据集中的777,000多条对话。研究发现,大约5%的用户回合表现出针对AI的敌意、侮辱、威胁或胁迫。有趣的是,用户敌意在不同模型之间存在显著差异,这似乎是由于吸引到每个模型的用户群体造成的,而不是模型自身的行为。研究还指出,AI的道歉与用户敌意的增加有关,但总体上道歉更频繁的模型受到的敌意却更少。 AI

影响 强调了需要采取强有力的安全措施,这些措施不仅要考虑AI对用户的伤害,还要考虑用户对AI的虐待。

排序理由 该集群包含一篇详细介绍研究结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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研究显示用户在5%的对话中虐待AI聊天机器人

本文如何被排名

Signal score
17 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
该集群包含一篇详细介绍研究结果的学术论文。[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, safety
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]

  1. arXiv cs.AI TIER_1 English(EN) · Fanqi Zeng, Sadid A. Hasan, Chaocheng He ·

    用户与人工智能的虐待是如何在对话系统中发生并产生影响的?

    arXiv:2609.13579v1 Announce Type: new Abstract: Safety research often focuses on model-generated harms, but users may also direct hostility, coercion, and adversarial pressure at models. Understanding how and when that occurs is essential for accurately interpreting model behavio…