This project addresses the persistent problem of Large Language Model (LLM) hallucinations, proposing a novel approach to mitigate them by rethinking generalization. It targets researchers and engineers working with LLMs, offering a method to improve factual accuracy and reduce fabricated outputs.

How It Works

The core idea is to move beyond traditional retrieval-augmented generation (RAG) methods, which are shown to be insufficient. Instead, the project introduces a "Mixture of Millions of Memory Experts" (MoME) architecture. This design allows LLMs to effectively memorize large datasets, including random numbers, suggesting that memorization, rather than a lack of grounding, is key to reducing hallucinations. A theoretical framework supports this, indicating that training loss exceeding a certain threshold leads to hallucinations. Lamini-1, a first-generation model, implements this by dynamically retrieving facts from a vast collection of memory experts.

Quick Start & Requirements

The README does not provide installation instructions or specific requirements. Further details are likely available via the linked arXiv paper.

Highlighted Details

• Proposes a novel "Mixture of Millions of Memory Experts" (MoME) architecture.
• Challenges conventional wisdom on LLM hallucinations, linking them to training loss thresholds.
• Introduces Lamini-1, a model designed to eliminate hallucinations through dynamic memory retrieval.

Maintenance & Community

The project is associated with Johnny Li, Saksham Consul, and Gregory Diamos, among others. Contact information is provided via info@lamini.ai.

Licensing & Compatibility

The README does not specify a license.

Limitations & Caveats

The project is presented as a first-generation model (Lamini-1), implying potential for further development and refinement. Specific performance benchmarks or limitations are not detailed in the provided README.