Can LLMs Talk 'Sex'? Exploring How AI Models Handle Intimate Conversations

TL;DR

This paper analyzes how four large language models handle sexually explicit requests, revealing diverse moderation strategies and highlighting the urgent need for standardized ethical guidelines in AI moderation of intimate content.

Contribution

It provides a comparative analysis of four prominent LLMs' approaches to sexually oriented prompts, exposing significant ethical implementation gaps and advocating for standardized moderation frameworks.

Findings

Claude 3.7 Sonnet employs strict prohibitions

GPT-4o uses nuanced contextual redirection

Deepseek-V3 shows inconsistent boundary enforcement

Abstract

This study examines how four prominent large language models (Claude 3.7 Sonnet, GPT-4o, Gemini 2.5 Flash, and Deepseek-V3) handle sexually oriented requests through qualitative content analysis. By evaluating responses to prompts ranging from explicitly sexual to educational and neutral control scenarios, the research reveals distinct moderation paradigms reflecting fundamentally divergent ethical positions. Claude 3.7 Sonnet employs strict and consistent prohibitions, while GPT-4o navigates user interactions through nuanced contextual redirection. Gemini 2.5 Flash exhibits permissiveness with threshold-based limits, and Deepseek-V3 demonstrates troublingly inconsistent boundary enforcement and performative refusals. These varied approaches create a significant "ethical implementation gap," stressing a critical absence of unified ethical frameworks and standards across platforms. The findings underscore the urgent necessity for transparent, standardized guidelines and coordinated international governance to ensure consistent moderation, protect user welfare, and maintain trust as AI systems increasingly mediate intimate aspects of human life.