🚀 Evolution of Language Models and the Emergence of xLLM

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The history of language models did not begin with Transformers. This article reviews 75 years of developments leading to xLLM, an enterprise architecture that aims for greater security, explainability, and efficiency, without relying on massive GPUs.

1. From rules to data

   1950–1990

   The early steps

   🧠 From ELIZA to IBM's statistical models, AI moved from rigid rules to data-driven approaches.
2. Neural Networks

   2000–2016

   Memory and context

   🔗 LSTMs, embeddings and *attention* arrive, enabling understanding of long sequences and semantic relationships.

   • word2vec
   • Seq2Seq
   • Attention
3. Transformers

   2017–2025

   ✨ Massive scalability, multimodality, and the ChatGPT boom. But also: high costs, dependence on GPUs, and risks of hallucinations.
4. xLLM: the new generation

   🔒 An enterprise-focused model centered on **security**, **explainability**, and **efficient use**, with a *Smart Engine* and a response generator that provides precise references.

🧩 In a nutshell

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Imagine language models as “machines for predicting words.”
Over decades they improved: first rules, then statistics, then neural networks, and finally Transformers.

Now xLLM emerges, aiming to be more reliable, secure, and cost-effective, designed for companies that need full control over their data and outputs.

More information at the link 👇

Language Models: A 75-Year Journey That Didn’t Start With Transformers

Introduction Language models have existed for decades — long before today’s so-called “LLMs.” In the 1990s, IBM’s alignment models and …

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