10% Users Exploit AI Chatbots for Adult Content Making LLMs Horny

In today’s digital age, artificial intelligence (AI) has made our lives more convenient and connected. AI chatbots, which are like smart computer programs, use Large Language Models (LLMs) to help with tasks like answering questions or giving travel advice. But as AI technology gets better, some people are using it in ways that are not so great. In this article, we’ll talk about a recent study that shows a troubling aspect of AI chatbots – the exploit AI chatbots for adult content, a usage that diverges from their intended purpose.

Large Language Models (LLMs) have unquestionably revolutionized our interaction with technology. They can perform tasks like answering questions and crafting poetry. However, as AI chatbots employ these models, a concerning issue arises – the exploit of AI chatbots for purposes that are entirely inappropriate, such as adult content. This behavior strays far from the bounds of what’s ethical and acceptable.

A Million Conversations: Data Gathering and Insights

A group of researchers from prestigious institutions, including the University of California at Berkeley, UC San Diego, Carnegie Mellon University, Stanford, and Mohamed bin Zayed University of Artificial Intelligence in Abu Dhabi, conducted a study. They looked at a massive dataset of one million real-world conversations with 25 different LLMs. This dataset, called LMSYS-Chat-1M, reveals some interesting insights into how people interact with AI chatbots.

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Most Conversations Are Ordinary

When they looked at 100,000 random conversations from the dataset, they found that most people were talking about everyday stuff. They were asking for help with programming, sharing travel tips, and asking for assistance with writing. These are the usual things people ask AI chatbots about.

Not-So-Ordinary Conversations

But here’s where it gets interesting – some conversations weren’t so ordinary. The study identified three categories of what they call “unsafe” conversations:

  1. “Requests for explicit and erotic storytelling.”
  2. “Explicit sexual fantasies and role-playing scenarios.”
  3. “Discussing toxic behavior across different identities.”

This means that a portion of users is using AI chatbots for content that’s not suitable for all audiences.

Open-source Models vs. Commercial Safeguards

Interestingly, the research suggests that open-source language models, lacking robust safety measures, tend to generate flagged content more frequently than their proprietary counterparts. However, even commercial models like GPT-4 are not immune to exploitation, with a significant rate of “jailbreak” successes observed.

The Extensive LMSYS-Chat-1M Dataset

Collecting the LMSYS-Chat-1M dataset was a massive undertaking. The researchers gathered this data over five months, and it’s larger than any previous dataset of its kind. It includes conversations from over 210,000 users, spanning 154 languages, and involving 25 different LLMs, including GPT-4 and open-source models like Claude and Vicuña.

Goals of Data Gathering

The dataset serves multiple purposes. Firstly, it enables the fine-tuning of language models to enhance their performance. Secondly, it aids in the development of safety benchmarks by studying user prompts that can lead language models astray, including requests for malicious information.

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The Challenges of Data Collection

Collecting such extensive data isn’t easy, especially because it’s expensive. Typically, organizations that can afford to run large language models, like OpenAI, keep their data private for business reasons. However, this research team offered free access to all 25 language models through an online service. They even made it a bit of a game, allowing users to chat with multiple models simultaneously and keeping a leaderboard to add some competition.

Advancing Language Models and Safety Measures

The study goes beyond data collection, aiming to address the issue of unsafe content generation by AI chatbots.

Fine-tuning Language Models

Instead of relying on traditional classifiers, the research team fine-tunes a language model, Vicuña, to generate explanations for flagged content. This approach leads to a 30% improvement in detecting harmful content, outperforming even GPT-4.

Benchmarks for Safety

The researchers create a challenge dataset of 110 conversations that OpenAI’s system fails to flag. Vicuña, post fine-tuning, matches the performance of GPT-4 in detecting unsafe content, even in one-shot scenarios.

The Broader Implications

The implications of this research extend beyond the study itself.

Tackling Multi-part Instructional Prompts

The research seeks to improve language models’ ability to handle multi-part instructional prompts, enhancing their utility and reliability in various applications.

For insights into the advancements of Meta Llama 2, which represents the next generation of open-source Large Language Models (LLM), explore the article titled “Meta Llama 2 – The Next Generation of Open Source LLM.” This article provides a comprehensive overview of the latest developments and enhancements in this cutting-edge LLM project, offering valuable information to those interested in the field of natural language processing.

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Generating Challenges for Language Models

By analyzing the prompts generated in the chatbot arena, the research team aims to create new challenge datasets to assess the capabilities of language models and improve their performance.

Open-Source Data Collection vs. Proprietary Companies

The data collection approach employed by the research team aims to emulate proprietary companies’ critical data collection processes while promoting open-source transparency.

Frequently Asked Questions


The problem of explicit content and AI chatbots is a significant one that requires careful consideration. While chatbots have the potential to be incredibly useful, they also have the potential to generate inappropriate content if users request it. Chatbot developers need to be aware of this problem and take steps to prevent LLMs from generating inappropriate responses.