交谈式呈现模式增加了与ChatGPT信息搜索期间的可信度判断
Christine Anderl1, Stefanie H Klein2, Büsra Sarigül2
1Leibniz-Institut Für Wissensmedien (IWM), Schleichstraße 6, 72076, Tübingen, Germany. c.anderl@iwm-tuebingen.de.
Scientific reports
|July 25, 2024
概括
用户很难检测对话人工智能 (AI) 代理的不准确性. 将信息作为静态文本呈现显著改善了与动态AI交互相比的可信度评估,强调了呈现模式对错误信息检测的影响.
科学领域:
- 人与计算机的交互
- 信息科学 信息科学 信息科学
- 认知心理学 认知心理学
背景情况:
- 由大型语言模型 (LLM) 驱动的对话代理越来越多地用于信息检索.
- 确保用户能够准确评估LLM产生的信息的可信度至关重要,因为可能存在事实上的不准确.
- 了解影响用户信誉判断的因素对于减轻错误信息至关重要.
研究的目的:
- 调查信息的呈现方式 (对话代理与静态文本) 如何影响用户检测不准确性的能力.
- 在不同类型的对话代理 (基于文本,基于语音) 和静态在线百科全书中比较可信度判断.
- 确定LLM代理人的对话性质是否妨碍准确的可信度评估.
主要方法:
- 进行了两项预先注册的实验,参与者对准确和部分不准确信息的可信度进行了评分.
- 信息通过基于文本的动态LLM代理,基于语音的代理或基于静态文本的在线百科全书来呈现.
- 使用调解分析分析数据,以了解对话性质在信誉判断中的作用.
主要成果:
- 与基于文本和基于语音的对话代理商相比,当信息以静态文本形式呈现时,参与者在检测不准确性方面明显更好.
- 无论信息来源是否有品牌 (例如,ChatGPT,Alexa,维基百科) 或没有品牌,这种影响仍然存在.
- 调解分析支持了这样一个假设:LLM代理人的对话格式对准确的信誉评估构成威胁.
结论:
- 信息的呈现方式极大地影响了用户辨别事实准确性的能力.
- 谈话界面,尽管具有动态性质,但可能无意中减少用户对信息的批判性评估.
- 未来的研究和设计应该考虑呈现方式对有效打击错误信息的影响.
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