Secret Use of Large Language Model (LLM)

TL;DR

This paper investigates the secret use of Large Language Models (LLMs), revealing that task types influence users' secrecy driven by external judgment, with implications for promoting transparency in AI usage.

Contribution

It provides empirical insights into why users secretly use LLMs and how task types affect their disclosure behavior, informing future transparency interventions.

Findings

Secret use often triggered by specific task types

Task types influence perceived external judgment

Behavior consistent across demographics and personality traits

Abstract

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.