揭秘用户偷偷用大模型的现象及其背后原因
Secret Use of Large Language Model (LLM)
- 通过调查和实验发现，特定任务易引发用户隐瞒使用大模型的行为
- 用户是否隐瞒主要受外界评价预期影响，而非个人特征
- 研究结果可指导设计促进AI透明使用的干预措施
大语言模型（LLM）的发展使AI使用透明度的责任分散。具体而言，用户被鼓励或要求在各种实际任务中披露其生成内容的来源。然而，一种新兴现象——用户秘密使用LLM——对确保终端用户遵守透明性要求构成了挑战。本研究采用混合方法，通过一项探索性调查（收集125个真实世界中的秘密使用案例）和针对300名用户的控制实验，探究了秘密使用行为的背景与成因。研究发现，此类隐蔽行为通常由特定任务触发，且超越了用户的人口统计学与性格差异。任务类型通过影响用户对他人评价的预期，进而影响其是否采取隐蔽行为。研究结果为未来设计鼓励更透明披露大模型或其他人工智能技术使用情况的干预措施提供了重要启示。
原文摘要 · Abstract (English)
The advancements of Large Language Models (LLMs) have decentralized the responsibility for the transparency of AI usage. Specifically, LLM users are now encouraged or required to disclose the use of LLM-generated content for varied types of real-world tasks. However, an emerging phenomenon, users' secret use of LLM, raises challenges in ensuring end users adhere to the transparency requirement. Our study used mixed-methods with an exploratory survey (125 real-world secret use cases reported) and a controlled experiment among 300 users to investigate the contexts and causes behind the secret use of LLMs. We found that such secretive behavior is often triggered by certain tasks, transcending demographic and personality differences among users. Task types were found to affect users' intentions to use secretive behavior, primarily through influencing perceived external judgment regarding LLM usage. Our results yield important insights for future work on designing interventions to encourage more transparent disclosure of the use of LLMs or other AI technologies.
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