Remove Large Language Models Remove NLP Remove Responsible AI
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AI Learns from AI: The Emergence of Social Learning Among Large Language Models

Unite.AI

Since OpenAI unveiled ChatGPT in late 2022, the role of foundational large language models (LLMs) has become increasingly prominent in artificial intelligence (AI), particularly in natural language processing (NLP). This could redefine how knowledge transfer and innovation occur.

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Decoder-Based Large Language Models: A Complete Guide

Unite.AI

Large Language Models (LLMs) have revolutionized the field of natural language processing (NLP) by demonstrating remarkable capabilities in generating human-like text, answering questions, and assisting with a wide range of language-related tasks.

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LLMOps: The Next Frontier for Machine Learning Operations

Unite.AI

But more than MLOps is needed for a new type of ML model called Large Language Models (LLMs). LLMs are deep neural networks that can generate natural language texts for various purposes, such as answering questions, summarizing documents, or writing code.

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Revolutionizing large language model training with Arcee and AWS Trainium

AWS Machine Learning Blog

In recent years, large language models (LLMs) have gained attention for their effectiveness, leading various industries to adapt general LLMs to their data for improved results, making efficient training and hardware availability crucial. Continual Pre-Training of Large Language Models: How to (re) warm your model?

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A Guide to Mastering Large Language Models

Unite.AI

Large language models (LLMs) have exploded in popularity over the last few years, revolutionizing natural language processing and AI. What are Large Language Models and Why are They Important? Their foundational nature allows them to be fine-tuned for a wide variety of downstream NLP tasks.

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8 Trending and New Large Language Models to Keep an Eye On

ODSC - Open Data Science

We’re hearing a lot about large language models, or LLMs recently in the news. Because of this, LLMs have a wide range of potential applications, including in the fields of natural language processing, machine translation, and text generation. If successful, this could lead to more efficient NLP models in the future.

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Responsible AI Testing of Large Language Models

John Snow Labs

We describe the open-source LangTest library, which can automate the generation and execution of more than 100 types of Responsible AI tests. We then introduce Pacific AI, which provides a no-code interface for this capability for domain experts, as well as automating many of the best practices on how these tools should be used.