[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-classical-models-still-beat-llms-on-spreadsheet-predictions":10,"sections":35},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":24,"tags":25,"sources":30,"feedback":34,"feedback_at":22,"cost_usd":34,"total_tokens":34},6897,"classical-models-still-beat-llms-on-spreadsheet-predictions","Classical Models Still Beat LLMs on Spreadsheet Predictions","A new benchmark finds trained gradient-boosted models beat frozen LLMs using data already on hand in 86% of cases, often needing just a few hundred labels.","A new crossover benchmark says the smart move for most tabular-data prediction problems is still to train a classical model, not prompt an LLM.\n\nResearchers tested small GPT models against six families of classical machine-learning models across 18 tabular datasets, running 126 independent evaluations under eight different prompting setups. They measured a labeled-data crossover point - where a trained model's accuracy overtakes a frozen LLM working from a plain-English prompt with no training at all. Even giving the LLM its best possible prompt, a trained classical model won using no more data than a typical business already has on hand in 86% of cases. The crossover landed at a median of about 6% of the full training set, and in 40% of cases a trained model won using the smallest data sample tested.\n\nThat's a useful reality check against the current pitch for AI-in-a-spreadsheet features like Microsoft Copilot in Excel and Claude for Excel, which promise you can skip data collection and labeling entirely. The researchers also found that stuffing more few-shot examples into a prompt doesn't behave like real training - error rates don't follow the predictable power-law improvement that classical models show as they see more labeled rows.\n\nNone of this makes LLMs useless for spreadsheets - zero-shot labeling still earns its keep when you have no data and no time. But for a recurring business prediction problem, the paper's advice is blunt: collect a few hundred labels and train a gradient-boosted model instead of leaning on a chatbot.","[\"llms\",\"machine-learning\",\"tabular-data\",\"benchmarks\"]","2026-09-18T04:00:00.000Z","2026-09-18T21:35:43.950Z","2026-09-18T21:35:55.866Z","published",null,[],"ai",[26,27,28,29],"llms","machine-learning","tabular-data","benchmarks",[31],{"name":32,"url":33},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.20218",0,{"sections":36},[37,40,44,49,54,58,62,67,71,76,81,86,91,96],{"name":38,"slug":24,"count":39,"latest_published_at":18},"AI",4082,{"name":41,"slug":42,"count":43,"latest_published_at":18},"Security","security",661,{"name":45,"slug":46,"count":47,"latest_published_at":48},"Policy","policy",339,"2026-09-17T12:00:00.000Z",{"name":50,"slug":51,"count":52,"latest_published_at":53},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":55,"slug":56,"count":57,"latest_published_at":18},"Hardware","hardware",155,{"name":59,"slug":60,"count":61,"latest_published_at":18},"Science","science",125,{"name":63,"slug":64,"count":65,"latest_published_at":66},"Consumer Tech","consumer-tech",99,"2026-09-09T17:27:33.000Z",{"name":68,"slug":69,"count":70,"latest_published_at":18},"Dev Tools","dev-tools",78,{"name":72,"slug":73,"count":74,"latest_published_at":75},"Software","software",75,"2026-09-10T20:41:21.000Z",{"name":77,"slug":78,"count":79,"latest_published_at":80},"Startups","startups",55,"2026-09-09T23:14:29.000Z",{"name":82,"slug":83,"count":84,"latest_published_at":85},"Gaming","gaming",43,"2026-09-10T12:18:06.000Z",{"name":87,"slug":88,"count":89,"latest_published_at":90},"General","general",41,"2026-09-08T01:57:23.000Z",{"name":92,"slug":93,"count":94,"latest_published_at":95},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":97,"slug":98,"count":99,"latest_published_at":100},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]