A series of articles explores the technical underpinnings of how Large Language Models (LLMs) process and understand text. The author delves into various methods, from basic word counting and statistical techniques like TF-IDF and Markov chains to more advanced neural network approaches such as Word2Vec, MLPs, RNNs, and LSTMs. The series also touches upon the linguistic features and semiotic theories essential for comprehending LLM functionality, emphasizing that these models are sophisticated machines rather than conscious minds. AI
IMPACT Provides foundational knowledge on LLM text processing, linguistics, and semiotics for AI practitioners.
RANK_REASON The cluster consists of blog posts explaining technical concepts related to LLMs, rather than a primary release or significant industry event.
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- Derrida
- Ferdinand de Saussure
- LLM
- long short-term memory
- Markov chain
- Mastodon
- Morphology
- multilayer perceptron
- Peirce
- phonetics
- pragmatics
- recurrent neural network
- semantics
- Syntax
- tf–idf
- Word2vec
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