Open-source model and scorecard for measuring hallucinations in LLMs
Hi all! This morning, we released a new Apache 2.0 licensed model on HuggingFace for detecting hallucinations in retrieval augmented generation (RAG) systems. What we've found is that even when given a "simple" instruction like "summarize the following news article," every LLM that's available hallucinates to some extent, making up details that never existed in the source article -- and some of them quite a bit. As a RAG provider and proponents of ethical AI, we want to see LLMs get better at this. We've published an open source model, a blog more thoroughly describing our methodology (and…
In plain words
An open-source model and evaluation tool for detecting hallucinations in large language models, particularly in retrieval augmented generation systems. It identifies instances where LLMs fabricate details not present in source material during tasks like summarization. The tool includes an Apache 2.0 licensed model on HuggingFace, evaluation results comparing popular LLMs, and methodology documentation. Designed for developers and organizations building RAG systems who want to measure and improve hallucination rates in their models.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi all! This morning, we released a new Apache 2.0 licensed model on HuggingFace for detecting hallucinations in retrieval augmented generation (RAG) systems. What we've found is that even when given a "simple" instruction like "summarize the following news article," every LLM that's available hallucinates to some extent, making up details that never existed in the source article -- and some of them quite a bit. As a RAG provider and proponents of ethical AI, we want to see LLMs get better at this. We've published an open source model, a blog more thoroughly describing our methodology (and some specific examples of these summarization hallucinations), and a GitHub repository containing our evaluation from the most popular generative LLMs available today. Links to all of them are referenced in the blog here, but for the technical audience here, the most interesting additional links might be: - https://huggingface.co/vectara/hallucination_evaluation_mode... - https://github.com/vectara/hallucination-leaderboard We hope that releasing these under a truly open source license and detailing the methodology, we hope to increase the viability of anyone really quantitatively measuring and improving the generative LLMs they're publishing.
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