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Text normalization is the process of transforming text into a single canonical form that it might not have had before. Normalizing text before storing or processing it allows for separation of concerns, since input is guaranteed to be consistent before operations are performed on it. Text normalization requires being aware of what type of text ...
Since two letters are combined, the mojibake also seems more random (over 50 variants compared to the normal three, not counting the rarer capitals). In some rare cases, an entire text string which happens to include a pattern of particular word lengths, such as the sentence "Bush hid the facts", may be misinterpreted.
Although AI seems to be evolving rapidly, it faces many technical challenges. For example, in many cases the language used by AI is very vague, and thus confusing for the user to understand. In addition, there is a "black-box problem" [11] [10] in which there is a lack of transparency and interpretability in the language of AI outputs. In ...
Tokenizers, which convert text into tokens. Embedding layer, which converts tokens and positions of the tokens into vector representations. Transformer layers, which carry out repeated transformations on the vector representations, extracting more and more linguistic information. These consist of alternating attention and feedforward layers.
Meta AI (formerly Facebook) also has a generative transformer-based foundational large language model, known as LLaMA. [48] Foundational GPTs can also employ modalities other than text, for input and/or output. GPT-4 is a multi-modal LLM that is capable of processing text and image input (though its output is limited to text). [49]
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To convert a grammar to Chomsky normal form, a sequence of simple transformations is applied in a certain order; this is described in most textbooks on automata theory. [4]: 87–94 [5] [6] [7] The presentation here follows Hopcroft, Ullman (1979), but is adapted to use the transformation names from Lange, Leiß (2009).
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