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www.cold-takes.com | ||
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www.journalofdemocracy.org
AI with superhuman abilities could emerge within the next few years, and there is currently no guarantee that we will be able to control them. We must act now... |
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www.alignmentforum.org
> This post is part of my AI strategy nearcasting series: trying to answer key strategic questions about transformative AI, under the assumption that... |
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www.darioamodei.com
Confronting and Overcoming the Risks of Powerful AI |
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lilianweng.github.io
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... |
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