Edward Y. Chang’s Post

In the ever-evolving realm of generative AI, understanding and evaluating Large Language Models (LLMs) is more crucial than ever. As we delve deeper into this digital era, LLMs stand at the forefront, transforming our interaction with technology through personalized content, advanced data analysis, and beyond. 🔍 But with great power comes great responsibility. The path to harnessing the full potential of LLMs is paved with challenges, from overcoming biases to ensuring fairness and adaptability in rapid technological advancements. How do we ensure that these models are not just powerful, but also aligned with our ethical standards and societal needs? 🚀 Join us in exploring the intricate balance between innovation and practical application, as we embark on a journey through the multifaceted landscape of LLM evaluation. From automated evaluation methods to human judgment, from benchmark datasets to adversarial testing, this exploration is an invitation to understand the nuanced approach required for responsible AI development. 🔗 https://lnkd.in/gwdrG8UP #AI #GenerativeAI #MachineLearning #DataScience #ProCogia

Large Language Models Aren’t Truly "Intelligent." 🧠❓ As an AI/ML Consultant, I’ve encountered the hype surrounding Large Language Models (LLMs) more times than I can count. Clients often ask, "Are these models intelligent? Are we on the brink of AGI?" 🤔 The short answer? No, they’re not. ❌ It’s becoming increasingly clear that simply scaling these models won’t lead us to true general intelligence. We’ve hit a wall with current architectures—impressive in some ways, but far from the breakthrough we’re looking for. Many are holding out hope that the next iteration—GPT-5, for example—might bring us closer to AGI (Artificial General Intelligence). But here’s the reality: that’s likely not going to happen. 🚫 Recent research points out what some of us have suspected all along: the so-called "emergent abilities" of these models aren’t truly emergent. They’re a result of in-context learning, model memory, and a vast repository of linguistic patterns. In plain terms: what looks like intelligence is largely memorization and the skill of the person crafting the prompt. ✍️ Another critical point: just because a model can follow instructions doesn’t mean it has reasoning capabilities—or hidden, potentially dangerous abilities. Let’s not get caught up in the hype. LLMs are powerful tools, but they’re not on the brink of taking over the world. They’re a stepping stone, not the final destination. 🌍 #AI #MachineLearning #Consulting #LLM #FutureOfAI

In the rapidly evolving landscape of Large Language Models (LLMs), traditional benchmarks like the MMLU (Massive Multitask Language Understanding) have been the go-to for evaluating AI capabilities. However, a growing body of research suggests that relying solely on these scores to select LLMs for specific applications could be misleading and costly. 🔍 𝐖𝐡𝐲 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐬 𝐌𝐢𝐠𝐡𝐭 𝐌𝐢𝐬𝐥𝐞𝐚𝐝: A recent study, "When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards," underscores a critical vulnerability in the standard benchmarking approach. This research demonstrates how minor methodological changes can significantly shift LLM rankings by up to eight positions on leaderboards. Such volatility raises questions about the stability and reliability of benchmark-based assessments. 👩💻 𝐓𝐡𝐞 𝐋𝐢𝐦𝐢𝐭𝐚𝐭𝐢𝐨𝐧𝐬 𝐨𝐟 𝐌𝐂𝐐𝐬: Leaderboards often rely on multiple-choice questions (MCQs) to gauge model performance. While MCQs offer a straightforward and quantifiable way to test certain model capabilities, they fall short in measuring comprehensive performance. The research highlights that models might perform well on MCQs by recognizing patterns rather than genuinely understanding content, which doesn’t necessarily translate to effectiveness in practical applications. 🔄 𝐓𝐡𝐞 𝐬𝐭𝐮𝐝𝐲 𝐜𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐳𝐞𝐬 𝐚𝐝𝐣𝐮𝐬𝐭𝐦𝐞𝐧𝐭𝐬 𝐢𝐧𝐭𝐨 𝐬𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐞 𝐚𝐧𝐝 𝐮𝐧𝐬𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐞 𝐩𝐞𝐫𝐭𝐮𝐫𝐛𝐚𝐭𝐢𝐨𝐧𝐬 𝐭𝐨 𝐞𝐱𝐩𝐥𝐨𝐫𝐞 𝐭𝐡𝐞𝐢𝐫 𝐢𝐦𝐩𝐚𝐜𝐭 𝐨𝐧 𝐦𝐨𝐝𝐞𝐥 𝐫𝐚𝐧𝐤𝐢𝐧𝐠𝐬: - Sensitive Perturbations: Modifications like shuffling choice order, changing symbols, and alternative scoring drastically alter model standings, pointing to a fragility in how performance is currently measured. - Unsensitive Perturbations: Adjustments that tweak the relevance of in-context examples show minor performance variations and help mitigate bias, suggesting a need for more nuanced testing methodologies. 𝐅𝐨𝐫 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬 𝐥𝐨𝐨𝐤𝐢𝐧𝐠 𝐭𝐨 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞 𝐋𝐋𝐌𝐬 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞𝐢𝐫 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬, 𝐭𝐡𝐢𝐬 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐬𝐞𝐫𝐯𝐞𝐬 𝐚𝐬 𝐚 𝐜𝐚𝐮𝐭𝐢𝐨𝐧𝐚𝐫𝐲 𝐭𝐚𝐥𝐞: - Custom Evaluation: Assess LLM performance using your specific data and scenarios rather than relying exclusively on generalized benchmarks. This approach ensures the model aligns with your unique requirements. - Holistic Assessment: Consider a broader range of evaluations that go beyond MCQs to include tests that measure real-world task performance and contextual understanding. - Continuous Benchmarking: As LLM technology evolves, so should the methods we use to evaluate it. Businesses must stay updated on the latest research and adapt their evaluation strategies accordingly. Do you think traditional benchmarks suffice, or is there a need for innovation in evaluation methods? #ai #genai #innovation #technology #llms

Last year, Mistral demonstrated Europe's incredible potential with their groundbreaking "Mixture-of-Experts." This innovation not only highlighted Europe's leadership in AI development but also set a new standard for what is possible with advanced machine learning techniques. Now, the new "Mixture-of-Agents" technique promises efficient coevolution of Small Language Model finetuning and human skills. This technique introduces a novel architecture where multiple language models (agents) are organised in layers. Each agent utilises the outputs from the previous layer, enhancing the overall response generation process. This hierarchical structure allows for more nuanced and sophisticated language understanding and generation, as each layer refines and builds upon the information provided by the previous ones. This means that MoA chooses the best answer and combines elements from multiple responses to create a more comprehensive and accurate output. This sophisticated aggregation process results in enhanced performance and demonstrates the potential for more advanced language generation techniques. The full research is available here: https://lnkd.in/eXwd6g9Y. A big shoutout to Paul-Olivier Dehaye, Technology Staffing Group partner on AI, who has been instrumental in advancing these techniques. Paul has been actively working with hestia.ai, focusing on how "Mixture-of-Agents" can efficiently guide the coevolution of Small Language Model finetuning and human skills. His work emphasizes the practical applications and transparency of these techniques, dispelling myths around AGI. Additionally, Paul's tool, Argo (http://argo-g.pt), offers one of the closest experiences to trying out this innovative technology yourself, especially in Europe. Ready to embark on this new era?

🚀 Exploring the Evolution of Large Language Models (LLMs):🚀 In the rapidly advancing world of AI, Large Language Models (LLMs) are transforming how we interact with technology. Let's dive into the five levels of LLM sophistication and the visionary concept of LLM OS. The Five Levels of LLMs: A Pyramid of Progress Q&A: At the foundational level, LLMs can engage in basic question-and-answer sessions, providing conversational responses to inquiries. Chatbot: Building on Q&A, this level introduces short-term memory, allowing LLMs to recall conversation histories and offer more contextually aware responses. RAG (Retrieval-Augmented Generation): Here, LLMs access external knowledge bases via APIs to enrich responses with accurate, real-world information. Function Calling: At this level, LLMs enhance their utility by integrating external tools and services, such as performing live currency conversions through APIs. Agent: The most advanced level, where LLMs act as agents that can perform complex tasks across various platforms— from booking flights to writing blog posts. LLM OS Envisioning the future, we introduce the concept of LLM OS (Large Language Model Operating System). This revolutionary framework positions an LLM at the core of an ecosystem, interconnected with a multitude of tools and services. LLM OS represents a leap towards a more intuitive, language-based interaction model with technology, promising a seamless integration of AI in our daily tasks. As we stand on the brink of these exciting developments, the potential of LLMs to simplify and enhance our digital interactions is limitless , thrilled to see how LLM OS will redefine our engagement with the digital world. #AI #MachineLearning #LLMs #Innovation #Technology #FutureOfAI

It is mandatory to apply CoT to the predictive data frame. This technique is very very very important, Navigators. It should be committed into Law, when using GenAI to predict things beyond next token.

📢 Exciting developments in the world of language models! 🚀 The ongoing race in large language models has taken an interesting turn, shifting from massive models to smaller, more specialized ones. This shift allows for better performance in specific tasks while optimizing compute power and costs. 🔰 Phi-3, the small language model- addresses these challenges through meticulous data selection from diverse and informative sources. This ensures enhanced language understanding and reasoning capabilities. 💡 However, it's important to acknowledge that even small language models face challenges associated with responsible AI practices. This includes addressing factual inaccuracies, biases, inappropriate content generation, and safety concerns. Developers constantly work on data curation, refinement, and red-teaming to ensure these challenges are tackled effectively. We believe that Small Language Models (SLMs) like Phi-3 strike a balance between efficiency and performance. They provide lightweight and resource-efficient solutions for simpler tasks, while being mindful of responsible AI practices. We are excited about the evolving landscape of language models and remain committed to advancing AI technology with a strong emphasis on transparency, fairness, and safety. 🌐 #AI #LanguageModels #TechAdvancements #ResponsibleAI

Did you know that Large Language Models can be tricked to include specific things such as "Golden Gate Bridge" in all of their answers independend of the prompt? Read our latest blog post to know more about this and other important insights on Generative AI.

Surprise, Surprise! Symbl.ai's Nebula LLM knocks out Claude 3, GPT-4, and Llama 3 in the Emotional Intelligence Face-off! There's been a seismic shift in artificial intelligence. Large Language Models (LLMs) are now the MVPs, wielding unprecedented prowess... But guess what? One superstar is outshining them all – Nebula LLM! In a riveting showdown, Nebula not only held its own against Claude 3, GPT-4, and Llama 3, but emerged victorious! Checked out the jaw-dropping details yet? No? Well, what are you waiting for? https://lnkd.in/gnSC3ytb Let's start a conversation. Which AI advancements have knocked your socks off recently? Share your thoughts! —------------ PS - Stay tuned for more game-changing insights on AI, Business, and Productivity. PPS - Follow me, Tony Greco, as we navigate this together! ----------------------- #artificialntelligence #technology #innovation #ai

TestLLM.com for sale! Elevate Your Presence in the Rapidly Growing AI Market with TestLLM.com! TestLLM.com is your ticket to the thriving world of Large Language Models (LLM) testing, a pivotal aspect of the expanding artificial intelligence landscape. As the AI market is poised for explosive growth, your ownership of this domain positions you to ride the wave of opportunity. The AI Market is Booming The market for Large Language Models has witnessed rapid growth in recent years. Industry reports project substantial expansion, with the AI market expected to surge from USD 11.3 billion in 2023 to an impressive USD 51.8 billion by 2028, demonstrating a remarkable compound annual growth rate (CAGR) of 35.6%. Proven Value in "Test" Domains The word "Test" holds substantial value in the tech industry, as evidenced by recent domain sales: TestHome.com sold for $4,188 TestFly.com sold for $5,000 TestWebsite.com sold for $7,000 These sales underscore the significance and demand for "Test" domains, emphasizing their relevance and recognition. Why TestLLM.com? AI Relevance: "LLM" represents Large Language Models, a cornerstone of AI. This domain unmistakably signifies its connection to AI and advanced language processing. Captivating & Compact: TestLLM.com is succinct and easy to remember, making it an excellent choice for branding your AI testing services or products. Ride the Wave of AI Growth! As the market for Large Language Models skyrockets, TestLLM.com stands as your beacon in the AI testing revolution. #LLM #Ai #artificialintelligence #openai #GPT #GPT4 #Claude #domain #tech