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artificialanalysis.ai | ||
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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... | |
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codeincomplete.com
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| | | | | Personal Website for Jake Gordon | |
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www.javaadvent.com
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| | | | | If you're reading this, you're probably already using some LLM for coding. Maybe it's Copilot, maybe Claude Code, maybe Cursor with Gemini enabled (or Cursor's own model). You know the drill. Do you truly expect the announcement "We are worst than competitors?" The problem is that when someone asks, "Which model is best for Java?", [...] | |
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peterbloem.nl
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| | | [AI summary] The text provides an in-depth overview of the Transformer architecture, its evolution, and its applications. It begins by introducing the Transformer as a foundational model for sequence modeling, highlighting its ability to handle long-range dependencies through self-attention mechanisms. The text then explores various extensions and improvements, such as the introduction of positional encodings, the development of models like Transformer-XL and Sparse Transformers to address the quadratic complexity of attention, and the use of techniques like gradient checkpointing and half-precision training to scale up model size. It also discusses the generality of the Transformer, its potential in multi-modal learning, and its future implications across d... | ||
