/explore
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www.alignmentforum.org | ||
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joecarlsmith.com
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| | | | | We should try extremely hard to use AI labor to help address the alignment problem. | |
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www.lesswrong.com
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| | | | | Machine learning has been outpacing safety. Ten years ago, AlexNet pushed the boundaries of machine learning, and it was trained using only two GPUs.... | |
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joecarlsmith.com
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| | | | | A high-level picture of how we might get from here to safe superintelligence. | |
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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... | ||
