Comparing LLM Recommendations to Real-World Data

You need to check out the Agent Leaderboard on Hugging Face! One question that emerges in the midst of AI agents proliferation is “which LLMs actually delivers the most?” You’ve probably asked yourself this as well. That’s because LLMs are not one-size-fits-all. While models thrive in structured environments, others don’t handle the unpredictable real world of tool calling well. The team at Galileo🔭 evaluated 17 leading models in their ability to select, execute, and manage external tools, using 14 highly-curated datasets. Today, AI researchers, ML engineers, and technology leaders can leverage insights from Agent Leaderboard to build the best agentic workflows. Some key insights that you can already benefit from: - A model can rank well but still be inefficient at error handling, adaptability, or cost-effectiveness. Benchmarks matter, but qualitative performance gaps are real. - Some LLMs excel in multi-step workflows, while others dominate single-call efficiency. Picking the right model depends on whether you need precision, speed, or robustness. - While Mistral-Small-2501 leads OSS, closed-source models still dominate tool execution reliability. The gap is closing, but consistency remains a challenge. - Some of the most expensive models barely outperform their cheaper competitors. Model pricing is still opaque, and performance per dollar varies significantly. - Many models fail not in accuracy, but in how they handle missing parameters, ambiguous inputs, or tool misfires. These edge cases separate top-tier AI agents from unreliable ones. Consider the below guidance to get going quickly: 1- For high-stakes automation, choose models with robust error recovery over just high accuracy. 2- For long-context applications, look for LLMs with stable multi-turn consistency, not just a good first response. 3- For cost-sensitive deployments, benchmark price-to-performance ratios carefully. Some “premium” models may not be worth the cost. I expect this to evolve over time to highlight how models improve tool calling effectiveness for real world use case. Explore the Agent Leaderboard here: https://lnkd.in/dzxPMKrv #genai #agents #technology #artificialintelligence

I just came across a groundbreaking paper titled "Benchmarking LLMs in Recommendation Tasks: A Comparative Evaluation with Conventional Recommenders" that provides comprehensive insights into how large language models (LLMs) perform in recommendation tasks. The researchers from The Hong Kong Polytechnic University, Huawei Noah's Ark Lab, Nanyang Technology University, and National University of Singapore have developed RecBench - a systematic evaluation platform that thoroughly assesses the capabilities of LLMs in recommendation scenarios. >> Key Technical Insights: This benchmark evaluates various item representation forms: - Unique identifiers (traditional approach) - Text representations (using item descriptions) - Semantic embeddings (leveraging pre-trained LLM knowledge) - Semantic identifiers (using discrete encoding techniques like RQ-VAE) The study covers two critical recommendation tasks: - Click-through rate (CTR) prediction (pair-wise recommendation) - Sequential recommendation (list-wise recommendation) Their extensive experiments evaluated 17 different LLMs across five diverse datasets from fashion, news, video, books, and music domains. The results are eye-opening: - LLM-based recommenders outperform conventional recommenders by up to 5% AUC improvement in CTR prediction and a staggering 170% NDCG@10 improvement in sequential recommendation - However, these performance gains come with significant computational costs, making real-time deployment challenging - Conventional deep learning recommenders enhanced with LLM support can achieve 95% of standalone LLM performance while being thousands of times faster Under the hood, the researchers implemented a conditional beam search technique for semantic identifier-based models to ensure valid item recommendations. They also employed low-rank adaptation (LoRA) for parameter-efficient fine-tuning of the large models. Most interestingly, they found that while most LLMs have limited zero-shot recommendation abilities, models like Mistral, GLM, and Qwen-2 performed significantly better, likely due to exposure to more implicit recommendation signals during pre-training. This research opens exciting avenues for recommendation system development while highlighting the need for inference acceleration techniques to make LLM-based recommenders practical for industrial applications.

LLMs scored 95% on identifying medical conditions when tested alone. When real people used them for medical advice, accuracy dropped to 35%. A new randomized study in Nature Medicine tested whether large language models actually help the public make better medical decisions. 1,298 participants were given medical scenarios and asked to identify conditions and recommend next steps. GPT-4o, Llama 3, and Command R+ all performed well when directly prompted. They identified relevant conditions in 94.9% of cases and recommended correct disposition in 56.3% on average. But when participants used these same models for assistance, condition identification dropped below 34.5% and disposition accuracy fell to 44.2% (no better than the control group using search engines). The gap wasn't medical knowledge. It was interaction. Researchers analyzed conversation transcripts and found users provided incomplete information to models. Models sometimes misinterpreted context or gave inconsistent advice. Even when models suggested correct conditions, users didn't consistently follow recommendations. Standard medical benchmarks didn't predict this. Models achieved passing scores (>60%) on MedQA questions matched to scenarios but still failed in interactive testing. Performance on structured exams was largely uncorrelated to performance with real users. Simulated patient interactions didn't predict it either. When researchers replaced humans with LLM-simulated users, simulated users performed better (57.3% vs 44.2%) and showed less variation. Simulations were only weakly predictive of human behavior. Here’s what this means: Benchmark performance is necessary but insufficient. A model scoring 80% on medical licensing exams can produce 20% accuracy when paired with real users. The constraint isn't algorithmic capability. It's human-AI interaction design. Users don't know what information to provide. Models don't ask the right clarifying questions. Correct suggestions get lost in conversation. For clinicians: expect patients to arrive with AI-informed conclusions that may not be accurate. Patients using LLMs were no better at assessing clinical acuity than those using traditional methods. For developers: user testing with real humans must precede deployment. Simulations and benchmarks don't capture interaction failures. AI excels at medical exams. But medicine isn't a multiple-choice test. It's a conversation under uncertainty. — Source: Nature Medicine - "Reliability of LLMs as medical assistants for the general public"

🎯 From hallucinations to high accuracy: This study explores how pairing large language models (LLMs) with clinical guidelines using Retrieval Augmented Generation (RAG) impacts preoperative decision-making across 14 real-world scenarios. Published in npj Digital Medicine (Nature Portfolio), the study evaluated 10 LLMs: including GPT-4, Claude, Llama, and Gemini, enhanced with RAG to assess surgical fitness. By grounding outputs in local and international guidelines, the GPT-4 + RAG model achieved: 🔹 96.4% accuracy in assessing surgical fitness, surpassing human evaluators (86.6%) 🔹 Faster outputs (under 20 seconds vs. 10 minutes for humans) 🔹 Lower hallucination rates and improved consistency 🔹 Contextual adaptability, with responses tailored to local protocols 🔹 Potential to reduce surgical cancellations and streamline pre-op workflows 📌 While not a replacement for clinical judgment, this work highlights the potential of LLM-RAG systems to support more consistent, efficient, and safe decision-making in perioperative care. Full paper & Codebase link in comments 👇 ____________________________________________________ #health #healthcare #research #llm #ai #machinelearning

This paper evaluates whether large language models (LLMs) used in healthcare make biased clinical decisions based on patients' sociodemographic traits, even when medical details are identical. 1️⃣ The study analyzed over 1.7 million LLM outputs across nine models, using 1,000 emergency cases (real and synthetic), each altered to reflect 32 different demographic profiles while keeping clinical information constant. 2️⃣ LLMs consistently gave more urgent, invasive, or mental health-related recommendations for patients labeled as Black, unhoused, or LGBTQIA+, far beyond what was clinically warranted or suggested by physicians. 3️⃣ Mental health evaluations were recommended six to seven times more often for LGBTQIA+ patients and more than twice as frequently as for the neutral control group, despite identical symptoms. 4️⃣ High-income patients were more likely to be directed toward advanced diagnostic tests, while low- and middle-income patients received less thorough recommendations, despite having the same clinical case. 5️⃣ The magnitude of these differences, often many times greater than physician judgment, suggests that LLMs are influenced by demographic data in a way that may reproduce or amplify real-world healthcare disparities. 6️⃣ Biases appeared across all models tested, both open-source and proprietary, and were often more pronounced when intersecting traits like race and housing status were combined. 7️⃣ The authors stress the importance of auditing LLMs for bias and recommend combining better prompt engineering, direct clinician oversight, and community engagement to reduce inequitable care risks. ✍🏻 Mahmud Omar, Shelly Soffer, MD, Reem Agbareia, nicola luigi Bragazzi, Donald Apakama, Carol Horowitz, Alexander Charney, Robert Freeman, Benjamin Kummer, MD, Ben Glicksberg, Girish Nadkarni, Eyal Klang. Sociodemographic biases in medical decision making by large language models. Nature Medicine. 2025. DOI: 10.1038/s41591-025-03626-6 (Behind paywall)

Right now, most Data Scientist job postings highlight LLMs. But here’s the reality: only a small fraction of real business problems actually need them. ✅ Most challenges still call for classical ML, statistics, optimization, or causal inference. ✅ For tabular data and time series (the backbone of many industries), LLMs add little to no value. ✅ Use cases like forecasting, anomaly detection, demand planning, fraud detection, recommendations, and predictive maintenance are rarely best solved with LLMs. ✅ Forcing LLMs where they don’t fit leads to overengineering, inflated GPU costs, and poor ROI. LLMs are powerful, but they’re not a silver bullet. In fact, many problems can be solved more efficiently with regression models or traditional ML methods. The best data scientists know this truth: it’s not about the flashiest model—it’s about solving the right problem with the right tool.

LLMs Won’t Level Product - They’ll Widen the Gap In an IEEE study, LLMs beat humans on coverage by over 40% - but still produced fewer acceptable user stories. A new IEEE study tested 10 state-of-the-art LLMs in an interview-based, real-world style requirements process: generating and evaluating agile user stories. The good: - High coverage: Models captured 73–96% of the “ground truth” requirements, far exceeding human students. - Strong structural quality: LLMs excelled in language clarity and internal consistency. - Well-formed baselines: Useful for getting an initial set of clean, syntactically sound user stories on the page. For example, Claude 3.5 Sonnet showed only 2.20% defected stories in AQUSA checks. - Quality checks: With a clear evaluation framework, top models matched or exceeded human–human agreement when assessing story quality. The bad: - Lower diversity and creativity: Humans explored far more of the requirements space; students’ average diversity was ~98.6% versus much lower for models. - Weaker rationale and problem framing: Many models struggled to make the “why” explicit, scoring notably lower on Rationale Clarity. - Fewer stories passed acceptance quality checks: Common defects included vague rationales and “and/and/and” stories that broke atomicity. - Quality variability: Even strong models produced a notable share of unacceptable stories compared with ground truth and students. This reinforces what I’ve said for yonks - although the study didn’t test experts using LLMs directly, the patterns make the likely effect clear: To a non-domain expert, AI looks magical. In the hands of a domain expert, it’s powerful. Give an LLM to someone without deep product sense, and it flattens their output - polished but narrow and locked to common patterns. Give it to a product expert, and it accelerates them - turning their contextual judgement, creativity, stakeholder insight, business acumen, and product sense into more complete, higher-quality outcomes faster. LLMs are not a leveler, they’re an amplifier, and they will widen the gap.

I am sure you have heard by now... Microsoft’s MAI-DxO, a "medical super-intelligence agentic model with an orchestrator," achieved 80% diagnostic accuracy—four times higher than practicing physicians on 304 NEJM Group Clinicopathological Conference cases (https://lnkd.in/giCG-zZd). What the study (https://lnkd.in/gKph2SiT) shows: - MAI-DxO, an “orchestrator” thinks like a multidisciplinary team. - Similar diagnostic gains appear across diverse model families (OpenAI, Gemini, Claude, Grok, DeepSeek AI, Llama), suggesting the orchestrator’s strategy..not any single frontier model is the reason. Very Impressive...but to me it seems many creating these models believe that being a clinician is mainly about being a diagnostician.  That is quite far from reality. Other recent noteworthy studies: 🟢 Stanford University’s new MedHELM benchmark (https://lnkd.in/gjE5BH_c) shows frontier LLMs shine in note-writing and patient communications, yet stumble on billing codes and prior auths. 🟢 Hippocratic AI’s Real World Evaluation-LLM study (https://lnkd.in/gmHXJZem) needed 6,234 U.S. clinicians and >300 k conversations to push a patient-facing “care agent” past 99% correct-advice rates—that's a lot of resources. 🟢 Most studies of LLMs on healthcare tasks do not use real data. See study from Bedi et al (https://lnkd.in/gfuv8-vp) that showed across 519 papers published between 2022 and early 2024, only 5% drew on data generated during routine patient care.  🟢Epistemic uncertainty is a big issue for LLM adoption in healthcare. Ethan Goh, MD (https://lnkd.in/gvEQS4YX ) found that giving PCPs direct access to GPT-4 did not improve their diagnostic reasoning. Instead of LLM vs Physician, the real comparison should be Physician vs Physician ➕ LLM. Clinical work is a team sport that occurs over time, not a snapshot in which diagnosis is the primary focus: data gathering ➜ hypothesis generation ➜ negotiation of uncertainty ➜ patient-centered decisions➜ nuances to support the patient’s treatment or care plan---- performed iteratively, often over years. Can LLMs or agentic models help across a care continuum?.. as orchestrator of diagnostic tests, care plans, prevention specialist, or as better, more patient, less time constrained educator... “Did the team (human + model) make safer, more effective, ethical, and equitable choices?” Thoughts on: ▪️ How do we study and then teach clinicians to interrogate model epistemics—knowing when to trust, verify, or override? ▪️ Where will augmented workflows (ambient charting, preventative health) deliver the earliest ROI that benefits the patient and clinician? Let’s move the debate from “humans or algorithms” to “humans with algorithms, responsibly deployed.” Scott J. Campbell MD, MPH #UsingWhatWeHaveBetter

Your recommender guesses. Mine thinks twice. I just read a fresh agent recipe that turns recommendation into a two speed brain. It ranks fast. Then it reflects slow. It explains why it picked each item, learns from the miss, rewrites what it knows about you, and tries again. That loop is the unlock for thin data and cold start. Today most LLM recs feel like vibes. They match patterns and pray. This agent self audits in plain English, so you can see the why and fix drift fast. The training plan is simple and strong. First learn from a better reasoning model to anchor good habits. Then run light RL that rewards real ranking wins. Result. Better picks with a tiny slice of data and small models that still ship on modest hardware. This is why I am excited for Main Street use. Shops, clinics, firms with short histories can still get smart recs on day one. You can demo this on a single screen. Left side fast list with short reasons. Right side reflection note after feedback. Click once. Watch the memory update. Run the next list. Cleaner picks in minutes. If you build with n8n, Lovable, or your own stack, here is the playbook I would test this week. Keep preferences as a readable note, not a black box vector. Add a slow loop that rewrites that note after each click. Tie rewards to rank, not vanity metrics. Think fast, then think slow, then ship. This arXiv report breaks down a two step recommender that ranks fast, then reflects to update a simple text profile of the user.

Using LLMs for ideation is a very appealing approach in R&D, but how well do they actually perform? Now we have some real data, at least in one domain of computer science. A new study from Stanford University rigorously evaluated LLM-generated research ideas against those from human experts. They didn’t stop at judging the ideas: they recruited 43 expert researchers to actually implement the ideas, each spending over 100 hours on execution, and then evaluated the outcomes too. 📈 Before execution, LLM ideas were rated better than human ideas across the board: Novelty, Excitement, and Expected Effectiveness. 📉 After execution, the story flipped. LLM ideas lost more ground than human ideas, in some cases ending up worse overall. So what explains the flip? While LLM ideas often looked more compelling, execution revealed more flaws, such as unrealistic assumptions, missing experimental rigor, and poor generalizability, compared to their human counterparts. LLMs are still promising tools for R&D ideation, just not without limitations. I believe we need better evaluation frameworks, feasibility guardrails, and human-in-the-loop feedback to make them truly useful in real research settings. Of course, this study focused on computer science. Domains like #chemistry involve very different constraints and characteristics. I’d love to see similar work done there. 📄 The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas, arxiv, Jun 25, 2025 🔗 https://lnkd.in/e4sx2EWG