introduction.md

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keywords: LLM hallucination detection, hallucination leaderboard, RAG hallucination benchmark, UltraChat hallucination rate, Verify API, kluster.ai, factual accuracy of language models, large language model evaluation
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The **LLM Hallucination Detection Leaderboard** is a public, continuously updated comparison of how well popular Large Language Models (LLMs) avoid *hallucinations*, responses that are factually incorrect, fabricated, or unsupported by evidence. By surfacing transparent metrics across tasks, we help practitioners choose models that they can trust in production.

### Why does hallucination detection matter?

* **User Trust & Safety**: Hallucinations undermine confidence and can damage reputation.
* **Retrieval-Augmented Generation (RAG) Quality**:  In enterprise workflows, LLMs must remain faithful to supplied context. Measuring hallucinations highlights which models respect that constraint.
* **Regulatory & Compliance Pressure**: Upcoming AI regulations require demonstrable accuracy standards. Reliable hallucination metrics can help you meet these requirements.

### How we measure hallucinations

We evaluate each model on two complementary benchmarks and compute a *hallucination rate* (lower = better):

1. **HaluEval-QA (RAG setting)**: Given a question *and* a supporting document, the model must answer *only* using the provided context.
2. **UltraChat Filtered (Non-RAG setting)**:  Open-domain questions with **no** extra context test the model's internal knowledge.

Outputs are automatically verified by [Verify](https://platform.kluster.ai/verify) from [kluster.ai](https://kluster.ai/), which cross-checks claims against the source document or web results.

> **Note:** Full experiment details, including prompt templates, dataset description, and evaluation methodology, are provided at the end of this page for reference.

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Stay informed as we add new models and tasks, and follow us on [X](https://x.com/klusterai) or join Discord [here](https://discord.com/invite/klusterai) for the latest updates on trustworthy LLMs.