Post by Karel Macek, PhD, ACC (ICF)

💸 You’ve been there. You invest time, data, and GPU hours into testing a model… only to find the task was too easy or way too hard. What if you could know the likely score before you even start? 📄 A fresh preprint (just 5 days old, Sept 25, 2025) explores exactly this idea — estimating LLM benchmark performance from task descriptions alone. Instead of crunching GPUs, you give the model a task description (task, data, metric, prompt style)… and it predicts: 📈 Expected score (0–100) 🧠 Rationale 🎯 Confidence level There are three levels of maturity: 1️⃣ Regression baselines (embeddings + XGBoost) → cheap, shallow, ballpark. 2️⃣ GPT-5 (no search) → already strong, using reasoning + prior knowledge. 3️⃣ GPT-5 + Search → best results: the model queries related literature, synthesizes evidence, and outputs forecasts with calibrated confidence. Why this matters for industry: ⚡ Save costs: skip hopeless experiments. 🎯 Prioritize: focus on high-potential projects. 📝 Smarter pilots: size datasets before annotating thousands of samples. 🤝 Empower AI hubs to triage projects quickly. 💡 In insurance AI, this could mean forecasting whether GPT-5 will hit 80% OCR accuracy on claims forms before annotating a dataset, or whether fraud detection with LLMs is viable compared to domain-specific detectors. Big kudos 🙌 to the authors for their excellent paper: Jungsoo Park, Ethan Mendes, Gabriel Stanovsky, Alan Ritter. 👉 Look Before You Leap: Estimating LLM Benchmark Scores from Descriptions 📄 https://lnkd.in/en3pm22N This principle is simple but powerful: 👉 Don’t just evaluate — forecast, then evaluate. Curious to hear from you: 🔹 Where would you use performance forecasting in your domain? 🔹 Would you trust a model’s forecast to guide project spend? Marek Rathouský, Yana Oliinyk, Ashutosh Pandey, Radek Zeng Neznaj, Josef VODICKA, Katarina Cernova, Filip Hron, Michal Kecera, Jakub Kubala, Kristýna Lesáková, Danila Kossygin, Matt Johnson, Hardeep Arora, Teymur Azayev, Ph.D, Fangyuan Yu, Shubham Gupta, Seng Wee N., Aaron Lim, Jan Petrov, Jakub Szlaur, Jan Spidlen, Ondrej Cikhart. #AI #LLM #Evaluation #Forecasting #InsuranceAI #DataScience