  Retrieval Augmented Generation (RAG) techniques aim to mitigate
hallucinations in Large Language Models (LLMs). However, LLMs can still produce
information that is unsupported or contradictory to the retrieved contexts. We
introduce LYNX, a SOTA hallucination detection LLM that is capable of advanced
reasoning on challenging real-world hallucination scenarios. To evaluate LYNX,
we present HaluBench, a comprehensive hallucination evaluation benchmark,
consisting of 15k samples sourced from various real-world domains. Our
experiment results show that LYNX outperforms GPT-4o, Claude-3-Sonnet, and
closed and open-source LLM-as-a-judge models on HaluBench. We release LYNX,
HaluBench and our evaluation code for public access.
