文本主题和假设的人与人工智能作者对能力和质量评估的影响
Sebastian Proksch1, Julia Schühle1, Elisabeth Streeb1
1Department of Psychology, Eberhard Karls University Tuebingen, Tuebingen, Germany.
Frontiers in artificial intelligence
|June 17, 2024
概括
人们倾向于低与人类作者相比,ChatGPT撰写的文本的价值,无论主题是什么. 这种算法厌恶表明在采用大型语言模型来生成文本时需要谨慎.
科学领域:
- 人与计算机的交互
- 人工智能伦理学 人工智能伦理学
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 的公众看法是混合的,对技术进步的赞赏,但反对机器做出道德决定.
- 有限的研究存在于影响算法厌恶与欣赏对LLMs的特定条件.
- 了解用户对LLM产生的内容的看法对于他们的社会融入至关重要.
研究的目的:
- 调查感知作者 (人类与ChatGPT) 和文本主题 (道德与技术) 如何影响用户对LLM产生的内容的感知.
- 为了检查用户对作者能力,内容质量和分享人类文本的意图的评估,与ChatGPT对比.
主要方法:
- 一个随机控制的实验涉及164名参与者.
- 参与者评价了六篇文本 (三篇道德,三篇技术),这些文本归因于"人类作者"或"ChatGPT".
- 依赖变量包括作者认知的能力,内容质量和分享意图.
主要成果:
- 发现了作者权的重要主要影响,在所有措施中,与所谓的人类作者相比,ChatGPT的价值一直被值.
- 在作者能力,内容质量和分享意图方面,ChatGPT的得分低于人类作者.
- 对于感知到的作者能力,观察到一个小的互动效应,但对于内容质量或分享意图没有.
结论:
- 这些发现与算法厌恶相一致,表明对LLM产生的内容的普遍不信任或值,特别是与人类撰写的文本相比.
- 结果表明,在文本编写任务中广泛接受和采用LLM存在重大挑战.
- 需要进一步的研究来探索用户感知的细微差别和驱动算法对LLMs厌恶的因素.
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