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Microsoft Researchers Combine Small and Large Language Models for Faster, More Accurate Hallucination Detection

Marktechpost

Large Language Models (LLMs) have demonstrated remarkable capabilities in various natural language processing tasks. However, they face a significant challenge: hallucinations, where the models generate responses that are not grounded in the source material.

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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. Now you can launch a training job to submit a model training script as a slurm job.

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Enhancing Visual Search with Aesthetic Alignment: A Reinforcement Learning Approach Using Large Language Models and Benchmark Evaluations

Marktechpost

Modern vision models like CLIP and LDM, trained on large image-text pair datasets, demonstrate strong capabilities in semantic matching but may prefer images that do not align with user intents. Existing benchmarks for retrieval systems often need to pay more attention to evaluating aesthetics and accountable AI.

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Large Language Model Ops (LLM Ops)

Mlearning.ai

Introduction Create ML Ops for LLM’s Build end to end development and deployment cycle. Add Responsible AI to LLM’s Add Abuse detection to LLM’s. at main · balakreshnan/Samples2023 · GitHub BECOME a WRITER at MLearning.ai // invisible ML // 800+ AI tools Mlearning.ai First it starts with business problem to solve.

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LLMOps: The Next Frontier for Machine Learning Operations

Unite.AI

Machine learning (ML) is a powerful technology that can solve complex problems and deliver customer value. However, ML models are challenging to develop and deploy. This is why Machine Learning Operations (MLOps) has emerged as a paradigm to offer scalable and measurable values to Artificial Intelligence (AI) driven businesses.

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This NIST Trustworthy and Responsible AI Report Develops a Taxonomy of Concepts and Defines Terminology in the Field of Adversarial Machine Learning (AML)

Marktechpost

Artificial intelligence (AI) systems are expanding and advancing at a significant pace. The two main categories into which AI systems have been divided are Predictive AI and Generative AI. The well-known Large Language Models (LLMs), which have recently gathered massive attention, are the best examples of generative AI.

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This AI Paper from OpenAI Introduces the GPT-4o System Card: A Framework for Safe and Responsible AI Development

Marktechpost

The System Card provides a comprehensive framework for understanding and assessing GPT-4o’s capabilities, offering a more robust solution for the safe deployment of advanced AI systems. Check out the Paper and Details. All credit for this research goes to the researchers of this project.