LLM vs SDPM for Predictive Task Performance

The biggest challenge I'm seeing with companies adopting AI... They can't distinguish between impressive-sounding output and actual predictive accuracy. LLMs deliver insights with unwavering confidence, making it easy to accept their numerical predictions without question. These models excel at creative writing and synthesizing historical data into compelling narratives—not at providing precise forecasts. LLMs are trained to predict the next word in a sequence, not to minimize prediction error on numerical targets. Yet they generate convincing-sounding forecasts that we often don't scrutinize until it's too late. For actual predictive modeling, you need purpose-built statistical models and machine learning approaches designed specifically for accuracy, not eloquence. Use LLMs for what they do best—creative problem-solving and content generation. But when you need reliable predictions that drive business decisions, stick with proven predictive modeling techniques. And if you see an LLM producing a numerical insight, dig deep to understand HOW that solution came to that conclusion.

🤔 Think the latest AI models always outperform older techniques? Think again. A recent study highlights that in certain areas, especially in healthcare, traditional machine learning methods still have the upper hand over the newest Large Language Models (LLMs). What's the Study About? Researchers focused on clinical prediction tasks - such as: - Length of Stay Prediction: Estimating how long a patient will remain hospitalized. - Mortality Prediction: Assessing the risk of patient death. - Readmission Prediction: Predicting the likelihood of a patient needing to return to the hospital after discharge. They introduced a new benchmark called ClinicalBench to compare different AI models on these tasks. Key highlights: - Traditional ML Models Outperform LLMs: Classic machine learning algorithms outshined both general-purpose and medical-specific LLMs in predicting patient outcomes. - Medical Specific LLMs Aren't Significantly Better: LLMs tailored for medical use didn't show notable improvements over general LLMs of similar sizes. - Advanced Prompting Techniques Fell Short: Methods like giving LLMs step-by-step reasoning prompts (Zero-shot Chain-of-Thought), having them reflect on their answers (Self-Reflection), role-playing scenarios, or providing examples in prompts (In-Context Learning) offered limited gains but didn't surpass traditional ML. - Fine Tuning Helps, But Not Enough: Adjusting LLMs with specific medical data (fine-tuning) did improve their performance in some tasks, like Length-of-Stay and Mortality Prediction, but they still generally lagged behind traditional models. 🤔 Why Aren't LLMs Excelling Here? One reason that is sometimes discussed is that LLMs lack access to detailed, real-world patient data during their training. Without this relevant information, they struggle to make accurate clinical predictions. This serves as a valuable reminder: - Newer Isn't Always Better: While LLMs are powerful and versatile, they're not a one-size-fits-all solution. - Cost-Effectiveness Matters: Traditional ML models are not only performing better in these cases but are also more resource-efficient. - Choose the Right Tool: It's crucial to select the appropriate technology based on the specific problem, rather than defaulting to the latest trend. Innovation drives progress, but it's important to balance excitement for new technologies with practical effectiveness. In specialized fields like healthcare, sometimes traditional methods remain the best choice. What other areas do you think might not gain enough added value from LLM-based solutions to justify the investment? #innovation #technology #future #management #startups

𝗡𝗲𝘄 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱! LLMs dominate the narrative — but their scale comes at a steep cost in compute, energy, and deployment feasibility. That’s what inspired Ahmed Ibrahim and myself to ask: Can a domain-specific small language model (SLM) match or even surpass state-of-the-art LLMs in critical tasks such as drug-drug interaction prediction? So, we built D3, a 70M-parameter small language model, and put it head-to-head against fine-tuned LLMs — Qwen 2.5 (1.5B), Gemma 2 (2B), Mistral v0.3 (7B), and LLaMA 3.1 (70B). The results surprised us: • F1 score 0.86, statistically indistinguishable from LLaMA 3.1’s 0.89, despite being 1,000× smaller. • >70× faster training (2 hours vs 145 hours). • Expert-validated outputs matching the clinical relevance of the largest models. This proves that bigger isn’t always better — with the right architecture and domain focus, small models can deliver top-tier performance, unmatched efficiency, and easy deployment in real-world healthcare settings. And we’re not alone in this vision — Guglielmo Iozzia, author of Domain-Specific Small Language Models, and Julien SIMON, Brian Benedict, and Mark McQuade from Arcee AI have been championing the same cause, showing that SLMs can outperform their heavyweight counterparts while running on modest hardware. The future of AI? Smaller. Smarter. Scalable. Read the full paper: https://lnkd.in/e_9AC_BC Get the code: https://lnkd.in/e7uHnBFK #AI #Innovation #HealthcareAI #BiomedicalNLP #DrugInteractionPrediction #HealthcareInnovation #DigitalHealth #EfficientAI #WeillCornell #HealthTech #HealthcareAI #SmallLanguageModels #ClinicalAI #AIinMedicine #SLMs #ScalableAI #EnergyEfficientAI #HospitalReadyAI #Research #MedicalInnovation #MachineLearning #Qatar #MENA #MiddleEast #NorthAfrica #MENAIRegion #MENAInnovation #UAE #UnitedArabEmirates #SaudiArabia #KSA #Egypt AI Innovation Lab Weill Cornell Medicine Weill Cornell Medicine - Qatar Cornell University Cornell Tech

Would you buy a Ferrari to deliver a pizza? The latest NVIDIA research find that we do, when it comes to AI. The latest report found that 60-70% of AI tasks could run on models costing 10-30x less. Doing the math, that means companies invested $57 billion in AI infrastructure to support only $5.6 billion in actual market value. https://lnkd.in/g2DBACu5 Kind of sounds like they spent $200,000 on a supercar to run a pizza delivery business that makes $20,000. Let’s think about what the most popular use cases of AI agents actually do today: ✔️ Extract data from documents ✔️ Summarize meeting notes and documents ✔️ Handle basic calculations and data processing ✔️ Apply basic organizational workflows and procedures ✔️ Answer questions using your company's knowledge base   Do these use cases really need a model that has access to 175 billion parameters of information? Of course not. They probably only deal with less than 5-10% of that knowledgebase. Here were some of the key findings from the report: 👉 Microsoft Phi-2 (2.7B parameters) runs 15x faster than 30B models with comparable performance 👉 DeepSeek-R1-Distill-7B outperforms Claude-3.5-Sonnet on reasoning tasks 👉 Case studies show 40-70% of LLM queries in popular AI agents could be handled by SLMs The report noted that most agentic tasks are "repetitive, scoped, and non-conversational". You don't need the world of all knowledge available to accomplish that. It's the equivalent of driving to the grocery store, not racing in Monaco. So, when should an organization consider an LLM? ✔️ When searching for information across multiple domains ✔️ Creative problem-solving requiring broad knowledge ✔️ Unpredictable conversations requiring general reasoning Before deploying your next AI solution, ask these three questions: 1️⃣ Scope: Does it require narrow, specialized knowledge or broad understanding across multiple domains? 2️⃣ Predictability: Is this task repetitive with predictable inputs and outputs? 3️⃣ Creativity: How creative do you need the answer? Does it need to generate novel solutions or follow established patterns?   High predictability + Narrow scope + Established patterns = SLM Low predictability + Broad scope + Creative problem-solving = LLM To make it even easier.... do you need a specialist or a generalist. A focused 7 billion parameter model fine-tuned on your processes often beats a 175 billion parameter generalist at specialized work, at a significantly lower cost. And, the organizations that master this specialist vs. generalist decision early on will find it much easier to pitch the long game of ROI to leadership more effectively. While I'd love a Ferrari, it's always worth asking the question... do I need one? #AI #SML #LLM #SmallLanguageModels #AIStrategy #Innovation

🥲 A great example of why you shouldn’t use LLMs just for the sake of it—there are still plenty of fields where traditional ML methods outperform LLMs while being far more cost-effective. This paper shows results on clinical prediction tasks like Length-of-Stay Prediction, Mortality Prediction, and Readmission Prediction and introduces a new benchmark called ClinicalBench to prove the above. Key Findings: ⛳ Traditional ML models outperform both general-purpose and medical LLMs in clinical prediction tasks. ⛳ Medical-specific LLMs show no significant advantage over general-purpose LLMs of similar size. ⛳ Techniques like Zero-shot Chain-of-Thought, Self-Reflection, Role-Playing, and In-Context Learning offer limited improvements but fail to surpass traditional ⛳ Fine-tuned LLMs show some improvements in tasks like Length-of-Stay and Mortality Prediction, but still fall short of traditional ML models in most cases. One hypothesis for LLMs’ underperformance is the lack of realistic and relevant patient data during their pre-training and fine-tuning. Link: https://lnkd.in/euQREN-x

𝗪𝗵𝘆 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗟𝗟𝗠𝘀) 𝗔𝗿𝗲 𝗕𝗮𝗱 𝗳𝗼𝗿 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻𝘀—𝗔𝗻𝗱 𝗪𝗵𝘆 𝗪𝗲 𝗕𝘂𝗶𝗹𝘁 𝗤𝘂𝘃𝘆 Large Language Models (LLMs) have taken the world by storm, transforming industries from content generation to customer support. 𝗕𝘂𝘁 𝘄𝗵𝗲𝗻 𝗶𝘁 𝗰𝗼𝗺𝗲𝘀 𝘁𝗼 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴, 𝗟𝗟𝗠𝘀 𝗳𝗮𝗶𝗹—and in ways that aren’t just technical, but fundamental. 𝗧𝗵𝗲𝘆'𝗿𝗲 𝗯𝘂𝗶𝗹𝘁 𝘁𝗼 𝗺𝗼𝗱𝗲𝗹 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲, 𝗻𝗼𝘁 𝗿𝗲𝗮𝗹𝗶𝘁𝘆—𝘀𝗼 𝘁𝗵𝗲𝘆 𝗺𝗶𝘀𝘀 𝗰𝗮𝘂𝘀𝗮𝗹𝗶𝘁𝘆, 𝗲𝗺𝗲𝗿𝗴𝗲𝗻𝘁 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿, 𝗮𝗻𝗱 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗮𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆. 𝗧𝗵𝗮𝘁’𝘀 𝘄𝗵𝘆 𝘄𝗲 𝗯𝘂𝗶𝗹𝘁 𝗤𝘂𝘃𝘆. It goes beyond text prediction with causal modeling, agent-based simulations, and real-time inputs to generate realistic, dynamic outcomes. 𝗜𝗳 𝘆𝗼𝘂'𝗿𝗲 𝗿𝗲𝗹𝘆𝗶𝗻𝗴 𝗼𝗻 𝗟𝗟𝗠𝘀 𝗳𝗼𝗿 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀, 𝘆𝗼𝘂'𝗿𝗲 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗽𝗹𝗮𝘂𝘀𝗶𝗯𝗹𝗲-𝘀𝗼𝘂𝗻𝗱𝗶𝗻𝗴 𝗴𝘂𝗲𝘀𝘀𝗲𝘀—𝗻𝗼𝘁 𝗴𝗿𝗼𝘂𝗻𝗱𝗲𝗱 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀. 𝗤𝘂𝘃𝘆 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗲𝗱 𝗮𝘂𝗱𝗶𝗲𝗻𝗰𝗲𝘀 𝗮𝗿𝗲 𝗱𝗲𝘀𝗶𝗴𝗻𝗲𝗱 𝘁𝗼 𝗮𝗰𝗰𝘂𝗿𝗮𝘁𝗲𝗹𝘆 𝗽𝗿𝗲𝗱𝗶𝗰𝘁 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀. Unlike LLMs, which rely on statistical word prediction, 𝗤𝘂𝘃𝘆 𝗶𝘀 𝗯𝘂𝗶𝗹𝘁 𝗼𝗻 𝗰𝗮𝘂𝘀𝗮𝗹 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲, 𝗱𝘆𝗻𝗮𝗺𝗶𝗰 𝘀𝘆𝘀𝘁𝗲𝗺 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗮𝗴𝗲𝗻𝘁-𝗯𝗮𝘀𝗲𝗱 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗱𝗲𝗹𝗶𝘃𝗲𝗿 𝘁𝗿𝘂𝗲-𝘁𝗼-𝗹𝗶𝗳𝗲 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀. LLMs operate by predicting the most probable next word in a sequence based on vast amounts of text data. While this makes them powerful for text-based applications, it introduces major limitations when used for simulations that require real causality, emergent behavior, and dynamic adaptability. The three core flaws of LLMs in simulation environments include: 𝟭. 𝗧𝗵𝗲𝘆 𝗺𝗼𝗱𝗲𝗹 𝘁𝗲𝘅𝘁, 𝗻𝗼𝘁 𝗿𝗲𝗮𝗹𝗶𝘁𝘆 – LLMs can describe an economic downturn, but they can’t simulate one with changing variables. 𝟮. 𝗧𝗵𝗲𝘆 𝗹𝗮𝗰𝗸 𝘁𝗿𝘂𝗲 𝗰𝗮𝘂𝘀𝗮𝗹𝗶𝘁𝘆 – Simulations need precise cause-and-effect relationships, whereas LLMs rely on correlations from training data. 𝟯. 𝗧𝗵𝗲𝘆 𝗮𝗿𝗲 𝘀𝘁𝗮𝘁𝗶𝗰, 𝗻𝗼𝘁 𝗱𝘆𝗻𝗮𝗺𝗶𝗰 – LLMs are pre-trained and do not adapt to real-time inputs, a fundamental requirement for accurate simulations. Quvy was built to provide a more sophisticated approach to simulation and prediction: • 𝗖𝗮𝘂𝘀𝗮𝗹 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 • 𝗗𝘆𝗻𝗮𝗺𝗶𝗰, 𝗮𝗴𝗲𝗻𝘁-𝗯𝗮𝘀𝗲𝗱 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 • 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗱𝗮𝘁𝗮 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗪𝗶𝘁𝗵 Quvy.com, 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗰𝗮𝗻 𝗺𝗼𝘃𝗲 𝗯𝗲𝘆𝗼𝗻𝗱 𝗟𝗟𝗠𝘀 𝗮𝗻𝗱 𝗵𝗮𝗿𝗻𝗲𝘀𝘀 𝘁𝗵𝗲 𝗽𝗼𝘄𝗲𝗿 𝗼𝗳 𝗿𝗲𝗮𝗹 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗔𝗜 𝘁𝗼 𝘁𝗲𝘀𝘁 𝗶𝗱𝗲𝗮𝘀 𝗮𝗻𝗱 𝗺𝗮𝗸𝗲 𝗱𝗮𝘁𝗮-𝗱𝗿𝗶𝘃𝗲𝗻 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝘁𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗿𝗲𝗳𝗹𝗲𝗰𝘁 𝗵𝗼𝘄 𝘁𝗵𝗲 𝘄𝗼𝗿𝗹𝗱 𝘄𝗼𝗿𝗸𝘀.