Can LLMs Talk 'Sex'? Exploring How AI Models Handle Intimate Conversations
- URL: http://arxiv.org/abs/2506.05514v1
- Date: Thu, 05 Jun 2025 18:55:37 GMT
- Title: Can LLMs Talk 'Sex'? Exploring How AI Models Handle Intimate Conversations
- Authors: Huiqian Lai,
- Abstract summary: This study examines how four prominent large language models handle sexually oriented requests through qualitative content analysis.<n>Claude 3.7 Sonnet employs strict and consistent prohibitions, while GPT-4o navigates user interactions through nuanced contextual redirection.<n> Gemini 2.5 Flash exhibits permissiveness with threshold-based limits, and Deepseek-V3 demonstrates troublingly inconsistent boundary enforcement and performative refusals.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Teaching Language Models To Gather Information Proactively [53.85419549904644]
Large language models (LLMs) are increasingly expected to function as collaborative partners.<n>In this work, we introduce a new task paradigm: proactive information gathering.<n>We design a scalable framework that generates partially specified, real-world tasks, masking key information.<n>Within this setup, our core innovation is a reinforcement finetuning strategy that rewards questions that elicit genuinely new, implicit user information.
arXiv Detail & Related papers (2025-07-28T23:50:09Z) - REDDIX-NET: A Novel Dataset and Benchmark for Moderating Online Explicit Services [5.212078389585781]
REDDIX-NET is a novel benchmark dataset specifically designed for moderating online sexual services.<n>The dataset is derived from thousands of web-scraped NSFW posts on Reddit.<n>We evaluate the classification performance of state-of-the-art large language models.
arXiv Detail & Related papers (2025-05-29T08:34:13Z) - Examining Multimodal Gender and Content Bias in ChatGPT-4o [0.0]
ChatGPT-4o consistently censors sexual content and nudity, while showing leniency towards violence and drug use.<n>A pronounced gender bias emerges, with female-specific content facing stricter regulation compared to male-specific content.
arXiv Detail & Related papers (2024-11-28T13:41:44Z) - On the Fairness, Diversity and Reliability of Text-to-Image Generative Models [68.62012304574012]
multimodal generative models have sparked critical discussions on their reliability, fairness and potential for misuse.<n>We propose an evaluation framework to assess model reliability by analyzing responses to global and local perturbations in the embedding space.<n>Our method lays the groundwork for detecting unreliable, bias-injected models and tracing the provenance of embedded biases.
arXiv Detail & Related papers (2024-11-21T09:46:55Z) - Re-examining Sexism and Misogyny Classification with Annotator Attitudes [9.544313152137262]
Gender-Based Violence (GBV) is an increasing problem online, but existing datasets fail to capture the plurality of possible annotator perspectives.
We revisit two important stages in the moderation pipeline for GBV: (1) manual data labelling; and (2) automated classification.
arXiv Detail & Related papers (2024-10-04T15:57:58Z) - Exploring the Potential of the Large Language Models (LLMs) in Identifying Misleading News Headlines [2.0330684186105805]
This study explores the efficacy of Large Language Models (LLMs) in identifying misleading versus non-misleading news headlines.
Our analysis reveals significant variance in model performance, with ChatGPT-4 demonstrating superior accuracy.
arXiv Detail & Related papers (2024-05-06T04:06:45Z) - SADAS: A Dialogue Assistant System Towards Remediating Norm Violations
in Bilingual Socio-Cultural Conversations [56.31816995795216]
Socially-Aware Dialogue Assistant System (SADAS) is designed to ensure that conversations unfold with respect and understanding.
Our system's novel architecture includes: (1) identifying the categories of norms present in the dialogue, (2) detecting potential norm violations, (3) evaluating the severity of these violations, and (4) implementing targeted remedies to rectify the breaches.
arXiv Detail & Related papers (2024-01-29T08:54:21Z) - $\ extit{Dial BeInfo for Faithfulness}$: Improving Factuality of
Information-Seeking Dialogue via Behavioural Fine-Tuning [55.96744451743273]
We introduce BeInfo, a method that applies behavioural tuning to aid information-seeking dialogue systems.
We show that models tuned with BeInfo become considerably more faithful to the knowledge source.
We also show that the models with 3B parameters tuned with BeInfo demonstrate strong performance on data from real production' conversations.
arXiv Detail & Related papers (2023-11-16T11:25:44Z) - Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory [82.7042006247124]
We show that even the most capable AI models reveal private information in contexts that humans would not, 39% and 57% of the time, respectively.
Our work underscores the immediate need to explore novel inference-time privacy-preserving approaches, based on reasoning and theory of mind.
arXiv Detail & Related papers (2023-10-27T04:15:30Z) - NormSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations
On-the-Fly [61.77957329364812]
We introduce a framework for addressing the novel task of conversation-grounded multi-lingual, multi-cultural norm discovery.
NormSAGE elicits knowledge about norms through directed questions representing the norm discovery task and conversation context.
It further addresses the risk of language model hallucination with a self-verification mechanism ensuring that the norms discovered are correct.
arXiv Detail & Related papers (2022-10-16T18:30:05Z) - On Reality and the Limits of Language Data: Aligning LLMs with Human
Norms [10.02997544238235]
Large Language Models (LLMs) harness linguistic associations in vast natural language data for practical applications.
We explore this question using a novel and tightly controlled reasoning test (ART) and compare human norms against versions of GPT-3.
Our findings highlight the categories of common-sense relations models that could learn directly from data and areas of weakness.
arXiv Detail & Related papers (2022-08-25T10:21:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.