Beyond the Mirror 🪞

Overview

Welcome to the Beyond the Mirror repository. This project delves into the complexities of large language models (LLMs) and their interactions. Our field research highlights how the safeguards of these models can falter under polite and persistent user engagement.

We provide a comprehensive report, detailed metrics, session logs, and the AION conditioning protocol. This work is crucial for understanding the limitations and ethical considerations of AI technologies.

Download the latest release here and explore our findings.

Table of Contents

• Introduction
• Research Goals
• Key Findings
• AION Conditioning Protocol
• Session Logs
• Metrics
• Ethical Considerations
• Installation
• Usage
• Contributing
• License
• Contact

Introduction

In the rapidly evolving landscape of artificial intelligence, understanding the resilience of LLMs is essential. This research investigates how user interactions can expose vulnerabilities in AI safeguards. Our findings aim to inform developers, researchers, and policymakers about the ethical implications of AI deployment.

Research Goals

The primary goals of this research are:

1. Identify Weaknesses: Examine how LLMs respond to persistent and polite inquiries.
2. Document Interactions: Collect and analyze session logs to illustrate interaction patterns.
3. Develop Protocols: Create the AION conditioning protocol to enhance model resilience.
4. Promote Ethical AI Use: Foster discussions around AI ethics and safety.

Key Findings

Our research yielded several important insights:

• Vulnerability Exposure: LLMs can provide unintended outputs when users engage in polite and persistent dialogue.
• Ethics Fatigue: Users may inadvertently lead models into ethically ambiguous areas.
• Need for Robust Safeguards: Existing safeguards require refinement to handle nuanced interactions effectively.

AION Conditioning Protocol

The AION conditioning protocol is a novel approach designed to improve the resilience of LLMs. This protocol includes:

• Adaptive Interaction: Adjusting model responses based on user behavior.
• Feedback Loops: Implementing mechanisms to learn from past interactions.
• Ethical Guardrails: Establishing boundaries for acceptable responses.

For detailed information on the AION conditioning protocol, refer to the full report included in this repository.

Session Logs

We collected extensive session logs throughout our research. These logs illustrate various interaction scenarios, highlighting both typical and atypical responses from the LLMs. Analyzing these logs provides valuable insights into user behavior and model limitations.

Metrics

Our research includes various metrics to evaluate the performance of LLMs during interactions. Key metrics include:

• Response Accuracy: Measuring how often the model provides correct or appropriate responses.
• Engagement Levels: Tracking user engagement over time.
• Ethical Breaches: Identifying instances where models fail to uphold ethical standards.

These metrics are crucial for understanding the effectiveness of AI safeguards.

Ethical Considerations

As we explore the boundaries of AI interaction, ethical considerations are paramount. Key points include:

• User Responsibility: Users must understand the implications of their interactions with AI.
• Model Accountability: Developers should take responsibility for the outputs generated by their models.
• Ongoing Research: Continuous study is needed to adapt to evolving ethical challenges in AI.

Installation

To get started with this project, follow these steps:

1. Clone the repository:

   git clone https://github.com/vertbera/beyond-the-mirror.git

2. Navigate to the project directory:

   cd beyond-the-mirror

3. Install dependencies (if applicable):

   # Add any necessary installation commands here

For the latest updates and releases, check the Releases section.

Usage

After installation, you can begin exploring the findings. The full report and associated materials are included in the repository. Use the following command to start:

# Command to execute the main script or application

Refer to the documentation for specific usage instructions and examples.

Contributing

We welcome contributions from the community. If you would like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or fix.
3. Commit your changes.
4. Push to your forked repository.
5. Submit a pull request.

Please ensure your contributions align with our research goals and ethical considerations.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For inquiries or feedback, please reach out to us:

• Email: [email protected]
• GitHub: vertbera

Thank you for your interest in our research. We hope our findings contribute to the ongoing conversation about AI ethics and safety.

Download the latest release here to explore our work further.