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Large Language Model

Improving Biomedical Named Entity Recognition through Dynamic Definition Augmentation: A Unique AI Method to Enhance Precision in Large Language Models

The practice of biomedical research extensively depends on the accurate identification and classification of specialized terms from a vast array of textual data. This process, termed Named Entity Recognition (NER), is crucial for organizing and utilizing information found within medical literature. The proficient extraction of these entities from texts assists researchers and healthcare professionals in…

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Scientists at DeepMind have proposed an innovative self-training machine learning technique known as Naturalized Execution Tuning (NExT). It significantly enhances the ability of Language Models (LLMs) to infer about program execution.

Coding execution is a crucial skill for developers and is often a struggle for existing large language models in AI software development. A team from Google DeepMind, Yale University, and the University of Illinois has proposed a novel approach to enhancing the ability of these models to reason about code execution. The method, called "Naturalized…

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Transforming Web Automation: AUTOCRAWLER’s Novel Structure Boosts Effectiveness and Versatility in Changing Web Scenarios

Web automation technologies play a pivotal role in enhancing efficiency and scalability across various digital operations by automating complex tasks that usually require human attention. However, the effectiveness of traditional web automation tools, largely based on static rules or wrapper software, is compromised in today's rapidly evolving and unpredictable web environments, resulting in inefficient web…

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Improving the Scalability and Efficiency of AI Models: Research on the Multi-Head Mixture-of-Experts Approach

Large Language Models (LLMs) and Large Multi-modal Models (LMMs) are effective across various domains and tasks, but scaling up these models comes with significant computational costs and inference speed limitations. Sparse Mixtures of Experts (SMoE) can help to overcome these challenges by enabling model scalability while reducing computational costs. However, SMoE struggles with low expert…

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CATS (Contextually Aware Thresholding for Sparsity): An Innovative Machine Learning Structure for Triggering and Utilizing Activation Sparsity in LLMs.

Large Language Models (LLMs), while transformative for many AI applications, necessitate high computational power, especially during inference phases. This poses significant operational costs and efficiency challenges as the models become bigger and more intricate. Particularly, the computational expenses incurred when running these models at the inference stage can be intensive due to their dense activation…

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JP Morgan AI Research has unveiled FlowMind, an innovative machine learning method that utilizes the functions of language models like GPT to develop a system for generating workflows automatically.

In the world of automated processes in modern industries, a new advancement has been introduced named FlowMind by JP Morgan AI Research. This research's primary focus is on implementing methods of automating tasks that require flexibility and spontaneous decision-making, unlike the conventional robotic process automation (RPA) systems that handle more static and routine activities. Traditional RPA…

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Comprehending Essential Terms within the Extensive Language Model (LLM) Domain

Understanding the terminology and mechanisms behind Large Language Models (LLMs) is essential for venturing into the broader AI landscape. LLMs are sophisticated AI systems primed on vast text datasets to comprehend and produce text with human-like nuance and context. They deploy deep learning techniques to process and generate contextually appropriate language. High-profile examples of LLMs…

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