Contributing Writer @ Athina AI Hub
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Query construction forms the backbone of modern information retrieval systems, particularly in Retrieval-Augmented Generation (RAG). At its core, query construction transforms natural language inputs into structured queries that databases and retrieval systems understand. This transformation bridges the communication gap between humans and machines, enabling more accurate and relevant information retrieval.
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Organizations increasingly deploy Large Language Models (LLMs) across applications, making effective evaluation crucial. While these models excel at language processing, assessing their performance poses unique challenges that traditional methods struggle to address. Language evaluation of LLMs faces three critical challenges. First, while LLMs can generate grammatically flawless text, they may
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Parameter-efficient fine-tuning (PEFT) methods address the challenge of fine-tuning large language models (LLMs) for specific downstream tasks when the cost of training all model parameters becomes prohibitive. Fine-tuning large language models with billions of parameters can be computationally expensive, require significant storage, and lead to overfitting, especially when adapting to
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Language models like GPT-4 have established themselves as the leading standard in the NLP industry for developing advanced applications. These models can perform diverse tasks and easily adapt to new tasks using Prompt Engineering Techniques. However, they also present a massive challenge around training and fine-tuning. Due to their massive
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Large language models (LLMs), such as GPT-3 and LaMDA, are at the forefront of natural language processing (NLP). Trained on terabytes of text data, they generate human-like text and power multiple applications, including chatbots, virtual assistants, and search engines. This initial training process, where the LLM learns general language representations
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After completing the development and validation phase, model deployment is the next important step in the LLMOps workflow. Successful deployment requires careful consideration of efficiency, scalability, and the specific needs of the deployment environment. Quantization: Optimization Technique for Efficient Deployment of LLMs Quantization is a technique used to reduce LLMs&
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Managing large language models (LLMs) through LLMOps workflows requires a structured approach to ensure effective development, deployment, and maintenance. These workflows are divided into several key phases: data collection, model development, deployment, and governance, each crucial to the performance and sustainability of LLM systems. Specific methodologies, tools, and best practices
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The world is experiencing a transformative wave driven by large language models (LLMs). These advanced AI models, capable of understanding and generating human-quality text, are changing interactions with technology and information. LLMs power various applications, from chatbots and virtual assistants to content creation tools, advanced search engines, and even personalized
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Introduction OpenAI's mission to create artificial general intelligence (AGI) that is smarter than humans is still futuristic. However, tools like BabyAGI and AutoGPT - AI agents powered by LLMs - have already raised concerns. These AI agents use LLM's reasoning engine to plan their thinking journey
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Introduction In an era where data doubles every two years, sifting through mountains of information to find exactly what you need can feel like searching for a needle in an ever-expanding haystack. Yet, this is the challenge that modern artificial intelligence (AI) systems, like Retrieval-Augmented Generation (RAG), are designed to