Open-source model and scorecard for measuring hallucinations in LLMs
Published:Nov 6, 2023 19:11
•1 min read
•Hacker News
Analysis
This Hacker News article announces the release of an open-source model and evaluation framework for detecting hallucinations in Large Language Models (LLMs), particularly within Retrieval Augmented Generation (RAG) systems. The authors, a RAG provider, aim to improve LLM accuracy and promote ethical AI development. They provide a model on Hugging Face, a blog detailing their methodology and examples, and a GitHub repository with evaluations of popular LLMs. The project's open-source nature and detailed methodology are intended to encourage quantitative measurement and improvement of LLM hallucination.
Key Takeaways
- •An open-source model is available for detecting hallucinations in LLMs.
- •The model is designed for use with Retrieval Augmented Generation (RAG) systems.
- •The project includes a blog detailing the methodology and examples.
- •A GitHub repository provides evaluations of popular LLMs.
- •The goal is to improve LLM accuracy and promote ethical AI.
- •The open-source nature encourages quantitative measurement and improvement.
Reference
“The article highlights the issue of LLMs hallucinating details not present in the source material, even with simple instructions like summarization. The authors emphasize their commitment to ethical AI and the need for LLMs to improve in this area.”