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Unlock the potential of large language models with the 'Better Together' approach. Check this blog by TechRadar Pro to understand how collaboration enhances evaluation and drives innovation! #LLM #ArtificialIntelligence #AI #HumanxAI #SoftwareDevelopment #CustomSoftwareSolutions #Innovation #TechTrends

Unlock the potential of large language models with the 'Better Together' approach. Check this blog by TechRadar Pro to understand how collaboration enhances evaluation and drives innovation! #LLM #ArtificialInteligence #AI #Innovation #Tech #TechInnovation #TopTech

Have you ever wondered how large language models (LLMs) can read and comprehend text like humans? 🤔   At the core of an LLM's linguistic superpowers is the process of tokenization. LLM Tokens are the fundamental building blocks that allow these models to comprehend and generate human-like text with mind-boggling accuracy. 🧠 Tokenization involves breaking down text into smaller units, typically words or sub words called tokens. These tokens carry rich contextual information, allowing large language models (LLMs) like me to understand the relationships between words, phrases, and sentences more precisely. By analyzing these tokens, LLMs can comprehend the nuances of language and generate responses that are contextually appropriate.🚀   Curious to learn more? Check out my latest blog: https://lnkd.in/dTqnmDSi). 📚   #LLMTokens #LargeLangaugeModels #NaturalLanguageProcessing #AI #ArtificialIntelligence

Curious about Large Language Models (LLMs)? Here's a short and simple introduction to LLMs for anyone interested in understanding their impact and potential. Check out this article: https://lnkd.in/eMf5p-qh #AI #MachineLearning #LLM #Tech #ArtificialIntelligence

🌟 Evaluating Large Language Models: A Journey Beyond Benchmarks 🌟 The rise of Large Language Models (LLMs) has transformed how we approach intelligent applications—but their true potential lies in how we evaluate them. The latest Medium article dives into the nuanced art and science of LLM system evaluation—an iterative, dynamic, and essential process. Key Takeaways: ✅ LLM vs. LLM System Evaluation: It’s not just about the model. Real-world applications demand tailored datasets, prompt engineering, and performance validation. ✅ Offline + Online Evaluation: Combine controlled, dataset-driven offline tests with real-world online feedback for a robust framework. ✅ AI Evaluating AI: Leveraging LLMs for dataset generation and evaluation is promising, but requires critical oversight. ✅ Responsible AI (RAI): Ethical AI practices are crucial. Frameworks and metrics help ensure fairness, inclusivity, and safety. ✅ Application-Specific Metrics: Different use cases (summarization, Q&A, NER, Text-to-SQL) demand distinct evaluation strategies and metrics like BLEU, F1, InterpretEval, and more. 🔗 In the article, we explore frameworks like Prompt Flow, LangSmith, and more, while offering practical tips on setting up iterative evaluation pipelines and fostering responsible AI practices. 🚀 Whether you’re an AI practitioner, developer, or simply curious about the future of LLMs, this guide is for you. Let’s elevate the way we evaluate! 👉 Read the full article on Medium below. #AI #LLMs #MachineLearning #ResponsibleAI #Evaluation #NLP

Researchers Develop LongPPL and LongCE to Enhance Long-Context Language Model Evaluation 🤖📊🧠 https://lnkd.in/gguQDufq #LongPPL #LongCE #LanguageModels #Perplexity #AI #LLM #MachineLearning #Benchmarking #ArtificialIntelligence #TechInnovation Massachusetts Institute of Technology

"...a comprehensive analysis about the political preferences embedded in Large Language Models (LLMs)..." "...most conversational LLMs tend to generate responses that are diagnosed by most political test instruments as manifesting preferences for left-of-center viewpoints...." "... I demonstrate that LLMs can be steered towards specific locations in the political spectrum through Supervised Fine-Tuning (SFT) with only modest amounts of politically aligned data..." #AI, #ArtificialIntelligence, #MachineLearning, #BusinessStrategy, #DigitalTransformation

Large Language Models, Ontologies, and Hierarchical Knowledge Frameworks Author: John A. Yanosy Jr. Date: 10-05-2024 This is a first of a series of articles in which I will describe some of some of my ideas developed over the last twenty years in a framework of knowledge representation and reasoning and AI. In this first article I describe a general AI Knowledge Framework at a very high level, for the express purpose of reminding ourselves that knowledge is specific to human cognition, and not to machines. There must be a knower to have knowledge and a wise knower who can understand and apply knowledge with excellent and full understanding of the ethics associated with using knowledge. Responsibility resides with us to apply and use knowledge wisely, as well as the tools that augment our understanding and application thereof. With the above in mind, I now turn to a view of a hierarchical knowledge framework where my focus is to understand the relationships between data, information, knowledge, the role of Large Language Models (LLMs), and the crucial function of ontologies to create a sophisticated reasoning and context interpretation hierarchy. My model asserts that there is a progression from data to knowledge, with LLMs serving as intermediaries and ontologies providing essential structure, context, and explicit semantics (meaning) throughout this process. LLMs are used for augmenting human knowledge and help in creating ontologies, as well as discovering knowledge necessary for the higher level and purpose of knowledge in the framework. Ontologies as they are created at higher levels in the framework increasingly provide interpretation and models of reasoning reflective of the ultimate purpose of knowledge at the highest layer. This progression transforms data into useful information, which is then processed by LLMs, and interpreted by ontologies to generate content that bridges the gap between information and contextual knowledge.

🔍🤖📊 Scaling Large Language Models Makes Them Less Reliable, Producing Confident but Incorrect Answers https://lnkd.in/gsByEPdY #AI #LLMs #TechResearch #MachineLearning #AIDevelopment #LanguageModels #AIChallenges #AIInnovation #AITech #AIStudy 

An important vector that could possibly uncensor and jailbreak models. We talk about logit bias in this blog post giving an overview of how it can be abused. the rest is left to your mind to see how it can be abused 😘 #AI #LLM #ArtificialIntelligence #AIEthics #logit https://lnkd.in/dRtfNVDM