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www.shakudo.io | ||
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www.agentcloud.dev
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| | | | | Learn How to Build a RAG Chatbot Using AgentCloud And MongoDB | |
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weisser-zwerg.dev
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| | | | | Setting Up AI Models on Older Hardware - A Beginner's Guide to Running Local LLMs with Limited Resources | |
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bdtechtalks.com
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| | | | | Retrieval augmented generation (RAG) enables you to use custom documents with LLMs to improve their precision. | |
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lilianweng.github.io
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| | | Hallucination in large language models usually refers to the model generating unfaithful, fabricated, inconsistent, or nonsensical content. As a term, hallucination has been somewhat generalized to cases when the model makes mistakes. Here, I would like to narrow down the problem of hallucination to cases where the model output is fabricated and not grounded by either the provided context or world knowledge. There are two types of hallucination: In-context hallucination: The model output should be consistent with the source content in context. Extrinsic hallucination: The model output should be grounded by the pre-training dataset. However, given the size of the pre-training dataset, it is too expensive to retrieve and identify conflicts per generation. If w... | ||
