Artificial Inteligence ChatGPT Scalability 
Openxcell
JANUARY 20, 2025
LLM or large language models are deep learning models trained on vast amounts of linguistic data so they understand and respond in natural language (human-like texts). These encoders and decoders help the LLM model contextualize the input data and, based on that, generate appropriate responses.
Xebia
APRIL 16, 2025
Understanding the Value Proposition of LLMs Large Language Models (LLMs) have quickly become a powerful tool for businesses, but their true impact depends on how they are implemented. The key is determining where LLMs provide value without sacrificing business-critical quality.
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TechCrunch
MARCH 30, 2023
Called Fixie , the firm, founded by former engineering heads at Apple and Google, aims to connect text-generating models similar to OpenAI’s ChatGPT to an enterprise’s data, systems and workflows. “The core of Fixie is its LLM-powered agents that can be built by anyone and run anywhere.”
CIO
MARCH 13, 2023
It is clear that artificial intelligence, machine learning, and automation have been growing exponentially in use—across almost everything from smart consumer devices to robotics to cybersecurity to semiconductors. Going forward, we’ll see an expansion of artificial intelligence in creating.
TechCrunch
MAY 9, 2023
It’s often said that large language models (LLMs) along the lines of OpenAI’s ChatGPT are a black box, and certainly, there’s some truth to that. Even for data scientists, it’s difficult to know why, always, a model responds in the way it does, like inventing facts out of whole cloth.
CIO
DECEMBER 1, 2023
LexisNexis has been playing with BERT, a family of natural language processing (NLP) models, since Google introduced it in 2018, as well as Chat GPT since its inception. But now the company supports all major LLMs, Reihl says. “If We will pick the optimal LLM. We use AWS and Azure.
Perficient
NOVEMBER 20, 2024
Introduction to Multiclass Text Classification with LLMs Multiclass text classification (MTC) is a natural language processing (NLP) task where text is categorized into multiple predefined categories or classes. Traditional approaches rely on training machine learning models, requiring labeled data and iterative fine-tuning.
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