This library provides a method to attribute statements generated by Large Language Models (LLMs) back to specific segments of their provided context. It is designed for researchers and developers working with LLMs who need to ensure factual grounding and trace information provenance in generated text.

How It Works

ContextCite leverages a novel attention-based mechanism to identify and score the relevance of context segments to specific parts of an LLM's generated response. This approach allows for precise attribution, pinpointing the exact source information within potentially large documents that influenced a particular generated statement.

Quick Start & Requirements

• Install via pip: pip install context_cite
• Requires a CUDA-enabled GPU for optimal performance.
• Example notebooks are available for quickstart and RAG integration.

Highlighted Details

• Enables attribution of LLM-generated statements to source context.
• Provides a ContextCiter class for easy integration.
• Supports specifying attribution ranges within the response.
• Offers example notebooks for RAG chaining.

Maintenance & Community

• Maintained by Ben Cohen-Wang, Harshay Shah, and Kristian Georgiev.
• Links to a demo, blog posts, and the associated paper are provided.

Licensing & Compatibility

• The project is available under an unspecified license. Further clarification on licensing terms is recommended for commercial use.

Limitations & Caveats

The README does not explicitly state the license, which may impact commercial adoption. The primary model used in the example requires a CUDA-enabled GPU.