Evaluating LLMs: From Intelligence to Practical Usefulness

Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE Is AI’s increasing power coming at the cost of trust? 🤔 Generative AI is transforming industries, but the “black box” nature of models like LLMs is a serious concern, particularly in fields demanding accuracy – think healthcare, finance, and legal. Traditional RAG systems, while better, still lack transparency. That’s where KG-SMILE comes in. This groundbreaking research, detailed in a new arXiv paper (link in comments!), introduces a novel framework for *explainable* Knowledge Graph Retrieval-Augmented Generation. It uses controlled perturbations and weighted linear surrogates to pinpoint the most influential graph entities and relationships driving AI outputs. 🚀 The result? More stable, human-aligned explanations, boosting fidelity, faithfulness, and accuracy. KG-SMILE isn’t just about better results; it’s about building trust and understanding in AI. 💡 What are your thoughts on the importance of explainability in AI systems? Let’s discuss! 👇 #AI #ExplainableAI #KnowledgeGraph #RAG #MachineLearning #KG-SMILE Original article: https://lnkd.in/dWa8kbpc Automatically posted. Contact me if you want to know how it works :-)

Normality of data is often treated as a foundational assumption in AI and statistical modeling. In practice, however, real-world data rarely follows a perfect normal distribution; it is typically skewed or heavy-tailed. Interestingly, while the underlying data may be non-normal, the noise within it often tends to follow a normal distribution. This mismatch can cause AI models to gradually overfit to noise, leading to model drift. Such drift can be especially dangerous in high-stakes domains like finance and healthcare. To mitigate these risks, models in critical sectors should be retrained frequently and paired with continuous monitoring frameworks to ensure reliability and robustness. #AI

Mathematically speaking. The more elements/interconnections (RC) the AI generates, the greater the effort (RE) needed for review, the amount of money you pay, usually for wrong outputs (PC) and the (PR) production risk you need to handle. Ensuring quality requires proportional specialized knowledge (SK) to understand and validate the complexity of the content you produce. We can combine these into a total complexity cost function: total(RC)= w1⋅RE + w2⋅PR + w3⋅PC total(SK) depends asymmetrically on total(RC). Where W(i) are weighting factors representing the relative importance of each cost component. #ai

𝗟𝗟𝗠𝘀 𝘀𝗼𝘂𝗻𝗱 𝗯𝗿𝗶𝗹𝗹𝗶𝗮𝗻𝘁… 𝗯𝘂𝘁 𝗱𝗼 𝘁𝗵𝗲𝘆 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗿𝗲𝗺𝗲𝗺𝗯𝗲𝗿 𝗮𝗻𝘆𝘁𝗵𝗶𝗻𝗴? Every time we chat with a Large Language Model (LLM), it forgets the past. Unless we provide the history again, it treats each conversation as completely new. This makes us wonder: 🔹If they can’t remember, can we really call them intelligent? The truth is, today’s LLMs are not “thinking machines.” They are advanced pattern predictors — generating answers based on training data, not true memory or understanding. That’s why the future focus is on: 🔹𝘼𝙙𝙙𝙞𝙣𝙜 𝙢𝙚𝙢𝙤𝙧𝙮 so LLMs can recall past interactions. 🔹𝘽𝙪𝙞𝙡𝙙𝙞𝙣𝙜 𝘼𝙄 𝙖𝙜𝙚𝙣𝙩𝙨 that combine reasoning, tools, and memory to act smarter. Until then, LLMs are impressive, but not truly intelligent — more like excellent imitators. #AI #LLM #ArtificialIntelligence #MachineLearning #FutureOfAI #GenerativeAI #Innovation

🤔 What’s the difference between a good prompt and a great one? It’s not just creation, it’s rigorous testing. In my latest post (part 3 of my series), I dive into the science of prompt evaluation. I use 10 complexity levels to test LLMs, from lexical swaps to long-context inference. Prompts are designed to break (think hallucinated citations) to reveal model weaknesses. YAML metadata like complexityLevel and assertions ensures every test is measurable and repeatable. Evaluation isn’t sexy, but it’s what makes AI reliable. What’s your go-to method for testing prompts? Let’s discuss below! 👇 📌 Full guide in the first comment! #AI #PromptEngineering #TechInnovation #DataScience #MachineLearning

🤖 AI Hallucinations: Why They Happen and How to Mitigate Them 🔍 AI has revolutionized industries, but one persistent challenge threatens user trust: hallucinations. These occur when language models confidently generate information that sounds correct but is factually wrong. From legal briefs citing non-existent cases to medical models inventing conditions, the consequences are real and significant. In this insight, we explore: 💡 Why hallucinations are statistical inevitabilities in LLMs 💡 How current evaluation methods incentivize guessing over honesty 💡 Real-world examples highlighting the risks in law, healthcare, and business 💡 Emerging solutions such as RAG, confidence calibration, and multi-agent verification Building reliable AI is not just about bigger models, it is about calibrated systems that know when to abstain. 👉 Read the complete article to understand how the industry is working to reduce hallucinations and build trustworthy AI: https://lnkd.in/dpMtkYwx Follow us for more expert insights from Dr. Shahid Masood and the 1950.ai team. #AI #ArtificialIntelligence #AIHallucinations #TrustworthyAI #LanguageModels #TechnologyInnovation #1950ai #DrShahidMasood

Hallucinations in LLMs aren’t bugs—they’re warning signs waiting to be heeded. In our latest article for Prajna AI, “Hallucinations Are Not Bugs, They’re Warnings: Why Uncertainty Quantification Matters in LLMs,” we explore how hallucinated outputs are better seen as signals of uncertainty rather than model failure. We walk through Uncertainty Quantification (UQ) and introduce UQLM, an open-source toolkit that makes generation-time hallucination detection accessible to all—no fine-tuning, no extra data needed, and usable in real time. Discover how UQLM uses four key detection modes—consistency-based, token-probability-based, LLM-as-a-judge, and ensemble—to catch hallucinations and turn them into valuable alerts. Dive in, start building more trustworthy AI, and help us raise awareness of uncertainty-aware ML. ⭐ Check out the full article here: [https://lnkd.in/ggMCPjnv] Let's move from “silent failures” to informed warnings.— Prajna AI #AI #LLM #UncertaintyQuantification #UQLM #TrustworthyAI

Retrieval-Augmented Generation, a fascinating process, involves several key steps: - Creating external data - Retrieving relevant information - Augmenting the LLM prompt - Updating external data This method showcases the intricate interplay between data retrieval and generation, highlighting the importance of external sources in enhancing the output. #DataGeneration #Innovation #RAG #GenAI #AI #LLM

Artificial Intelligence isn’t coming — it’s already here. From predictive text to real-time traffic updates, AI quietly shapes our daily lives. At Warburton Capital Management, we believe it’s our responsibility to explore how these tools might help us prepare better, respond faster, and serve with greater precision — without ever replacing the human judgment at the core of our work. In our latest whitepaper, we share how we’re testing AI thoughtfully, where it might enhance our behind-the-scenes work, and why transparency matters as we adapt. 🔗 https://lnkd.in/gKB2fQJk #AI #WealthManagement #FinancialAdvisor

Large language models are reshaping industries from healthcare to finance. But true impact comes from rigorous evaluation—benchmarks, bias detection, and real-world testing. Discover best practices, tools, and challenges shaping LLM evaluation in 2025. 👉 Full insights here: https://iii.hm/1wsj #AI #LLMs #GenerativeAI #LLMOps #ArtificialIntelligence #MachineLearning AIMultiple