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The HHEM model is an open source model, created by [Vectara](https://vectara.com), for detecting hallucinations in LLMs. It is particularly useful in the context of building retrieval-augmented-generation (RAG) applications where a set of facts is summarized by an LLM, but the model can also be used in other contexts.
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If you are interested to learn more about RAG or experiment with Vectara, you can [sign up](https://console.vectara.com/signup/?utm_source=huggingface&utm_medium=space&utm_term=hhem-model&utm_content=console&utm_campaign=) for a free Vectara account.
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Now let's dive into the details of the model.
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## Cross-Encoder for Hallucination Detection
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The HHEM model is an open source model, created by [Vectara](https://vectara.com), for detecting hallucinations in LLMs. It is particularly useful in the context of building retrieval-augmented-generation (RAG) applications where a set of facts is summarized by an LLM, but the model can also be used in other contexts.
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If you are interested to learn more about RAG or experiment with Vectara, you can [sign up](https://console.vectara.com/signup/?utm_source=huggingface&utm_medium=space&utm_term=hhem-model&utm_content=console&utm_campaign=) for a free Vectara account.
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Vectara now implements an improved version of HHEM which is a calibrated score with longer sequence length, and non-English language support. See more details [here](https://vectara.com/blog/automating-hallucination-detection-introducing-vectara-factual-consistency-score/)
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Now let's dive into the details of the model.
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## Cross-Encoder for Hallucination Detection
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