Improved RAG for factual LLM responses using Wikipedia grounding
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WikiChat is a framework designed to mitigate Large Language Model (LLM) hallucinations by grounding responses in retrieved information from Wikipedia. It targets researchers and developers building factual, reliable chatbots, offering a robust 7-stage pipeline for enhanced accuracy and reduced factual errors.
How It Works
WikiChat employs a multi-stage pipeline that integrates information retrieval with LLM generation. It retrieves relevant passages from Wikipedia, extracts claims, grounds the LLM's response in these claims, and includes inline citations. This approach aims to ensure factual accuracy by explicitly linking generated text to verifiable sources, improving upon standard RAG systems.
Quick Start & Requirements
pixi
(https://pixi.sh/latest/#installation), and run pixi shell
to create and activate the environment. Install Docker.API_KEYS
and configure llm_config.yaml
.invoke
commands.inv demo --engine <your_llm_engine>
Highlighted Details
Maintenance & Community
The project is developed by Stanford University. Announcements and updates are provided in the README. Links to community channels are not explicitly mentioned.
Licensing & Compatibility
Code, models, and data are released under the Apache-2.0 license, permitting commercial use and linking with closed-source projects.
Limitations & Caveats
The free Wikipedia API is rate-limited and not suitable for production. Local LLM usage requires significant GPU resources. Compatibility with non-Linux systems (Windows, macOS) may require troubleshooting. Older distilled LLaMA-2 models are not compatible with versions >= 2.0.
3 months ago
Inactive