🧠 ai4org – GAN-based Hallucination Mitigation for Local Private LLMs

ai4org is a locally run, privacy-first framework that uses a Generative Adversarial Network (GAN) approach to detect and reduce hallucinations in custom Large Language Models (LLMs). Designed for organizations deploying their own LLMs on-premises, ai4org improves factual reliability while ensuring zero data leakage.

⚡ Your data. Your model. No cloud involved. 🤖 Powered by GANs: Generator = LLM, Discriminator = Truth Checker.

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🌟 Key Features

• 🛡️ Privacy-Preserving: Everything runs locally — no API calls or external data sharing.
• 🧠 GAN Architecture: A discriminator challenges the LLM's outputs to reduce hallucinations over time.
• 🔁 Feedback Loop: The system fine-tunes itself based on discriminator rejection or optional human feedback.
• 🎯 Domain-Specific: Train on your organization’s internal data for maximum relevance and accuracy.
• 🧩 Modular Design: Works with most Hugging Face-compatible LLMs (Mistral, LLaMA, etc.).

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🔬 How It Works

At its core, ai4org functions like a GAN:

• Generator: A fine-tuned LLM that produces text based on organizational inputs.
• Discriminator: A binary classifier that detects hallucinations or factually incorrect content.
• Training Loop: If the discriminator flags the output, the generator is refined with new feedback (auto or manual).

               ┌────────────────────┐
               │    User Input      │
               └────────┬───────────┘
                        ▼
               ┌────────────────────┐
               │  Fine-tuned LLM    │  ◄──┐
               └────────┬───────────┘     │
                        ▼                 │ Feedback
               ┌────────────────────┐     │ (Reward / Penalty)
               │   Discriminator     │ ◄──┘
               └────────┬───────────┘
                        ▼
               ┌────────────────────┐
               │   Final Output     │
               └────────────────────┘

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📂 Project Structure

ai4org/
├── data/                 # Your internal datasets
├── llm_finetune/         # Generator (LLM fine-tuning)
├── discriminator/        # Discriminator to detect hallucinations
├── feedback_loop/        # Adversarial training and reinforcement
├── webapp/               # Optional local interface
├── utils/                # Shared tools and helpers
├── main.py               # Launch everything locally
└── README.md

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🛠️ Local Setup

1. Clone the repository:

git clone https://github.com/your-org/ai4org.git
cd ai4org

2. Create a virtual environment and activate it:

python3 -m venv .venv
source .venv/bin/activate

3. Install dependencies:

pip install -r requirements.txt

4. Run the system:

python main.py

✅ Make sure your local LLM weights are available. No external API required.

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📈 Roadmap

• Local fine-tuning with organization-specific data
• GAN-based feedback architecture
• Discriminator training loop
• Plug-and-play human-in-the-loop support
• UI for real-time review and validation
• Metrics dashboard for hallucination reduction tracking

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🧪 Example Use Case

A private healthcare organization fine-tunes an LLM on its medical documentation. The LLM outputs a treatment suggestion. The discriminator flags the suggestion as inconsistent with training data. The LLM is penalized and retrained — improving future outputs.

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👨‍💻 Contributing

We welcome open-source contributions, especially in the areas of:

• Discriminator model improvement
• GAN training stability
• Dataset preparation and augmentation
• Dashboard and visualization

CONTRIBUTING.md coming soon.

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📜 License

Licensed under the MIT License. See LICENSE.

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🙏 Acknowledgments

Inspired by:

• GAN architectures applied to NLP
• Ongoing research in hallucination mitigation
• Open-source LLM communities and tools