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Harald KlinkeAndrey Markov & Claude Shannon Counted Letters to Build the First Language-Generation Models Shannon’s said: “OCRO HLI RGWR NMIELWIS” <a href="https://det.social/tags/Shannon" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Shannon</a> <a href="https://det.social/tags/Markov" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Markov</a> <a href="https://det.social/tags/NLP" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#NLP</a> <a href="https://det.social/tags/AIhistory" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#AIhistory</a> <a href="https://det.social/tags/LanguageModeling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LanguageModeling</a> <a href="https://spectrum.ieee.org/andrey-markov-and-claude-shannon-built-the-first-language-generation-models" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://spectrum.ieee.org/andrey-markov-and-claude-shannon-built-the-first-language-generation-models</a>

khushnumaMastering these core NLP techniques is crucial for any data scientist dealing with text data. From tokenization to language modeling, each method serves a unique purpose in processing, analyzing, and extracting valuable insights from textual information.<a href="https://mastodon.social/tags/NLP" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#NLP</a> <a href="https://mastodon.social/tags/DataScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#DataScience</a> <a href="https://mastodon.social/tags/Tokenization" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Tokenization</a> <a href="https://mastodon.social/tags/LanguageModeling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LanguageModeling</a> <a href="https://mastodon.social/tags/TextAnalysis" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#TextAnalysis</a> <a href="https://mastodon.social/tags/TextMining" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#TextMining</a> <a href="https://mastodon.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#MachineLearning</a> read more: <a href="https://blogulr.com/khushnuma7861/topnlptechniqueseverydatascientistshouldknow-120682" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://blogulr.com/khushnuma7861/topnlptechniqueseverydatascientistshouldknow-120682</a>

Naomi SaphraNew <a href="https://sigmoid.social/tags/languagemodeling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#languagemodeling</a> <a href="https://sigmoid.social/tags/nlp" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#nlp</a> <a href="https://sigmoid.social/tags/ai" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ai</a> <a href="https://sigmoid.social/tags/paper" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#paper</a>, led by Angelica Chen! We break the steepest MLM training loss drop into *2* phase changes: first in internal grammatical structure, then external capabilities. Big implications for emergence, simplicity bias, and interpretability! <a href="https://arxiv.org/abs/2309.07311" rel="nofollow noopener noreferrer" target="_blank">https://arxiv.org/abs/2309.07311</a>

Victoria Stuart 🇨🇦 🏳️‍⚧️Revealing the structure of language model capabilities <a href="https://arxiv.org/abs/2306.10062" rel="nofollow noopener noreferrer" target="_blank">https://arxiv.org/abs/2306.10062</a>Building a theoretical understanding of the capabilities of large language models (LLMs) is vital for our ability to predict & explain the behavior of these systems. ... we analyzed data from 29 LLMs / 27 cognitive tasks. LLM are better explained by 3 well-delineated factors that represent reasoning, comprehension & core language modeling.<a href="https://mastodon.social/tags/LLM" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LLM</a> <a href="https://mastodon.social/tags/LargeLanguageModels" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LargeLanguageModels</a> <a href="https://mastodon.social/tags/reasoning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#reasoning</a> <a href="https://mastodon.social/tags/LanguageModeling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LanguageModeling</a> <a href="https://mastodon.social/tags/comprehension" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#comprehension</a> <a href="https://mastodon.social/tags/GPT" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#GPT</a>

Netherlands eScience Center<a href="https://akademienl.social/tags/LanguageModeling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LanguageModeling</a> is trending, to a large extent because of <a href="https://akademienl.social/tags/ChatGPT" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ChatGPT</a>. But did you know language modeling has been with us for more than a century? And that it was born of the collaboration of a poet and a mathematician? Our engineer Carsten Schnober tells us more: <a href="https://blog.esciencecenter.nl/language-modeling-the-first-100-years-357556816148" rel="nofollow noopener noreferrer" target="_blank">https://blog.esciencecenter.nl/language-modeling-the-first-100-years-357556816148</a>

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