{"version":1,"type":"story","url":"https://digestai.news/story/language-models-fall-short-for-quantitative-decision-tasks-proposing-large","json":"https://digestai.news/story/language-models-fall-short-for-quantitative-decision-tasks-proposing-large.json","markdown":"https://digestai.news/story/language-models-fall-short-for-quantitative-decision-tasks-proposing-large.md","slug":"language-models-fall-short-for-quantitative-decision-tasks-proposing-large","headline":"Language Models Fall Short for Quantitative Decision Tasks, Proposing Large Quantitative Models","summary":"The paper argues that relying on large language models (LLMs) for high‑stakes quantitative decisions—such as pricing, risk assessment, capital allocation, medical triage, or cyber‑security—is fundamentally flawed. LLMs learn from human‑written descriptions, which inevitably discard precise numerical information, making it impossible for any downstream model to reconstruct the missing data.\n\nThe authors formalize this limitation as a property of the training substrate rather than model size, and they identify three essential attributes for consequential applications: reproducibility of results, full lineage tracing from outputs back to original records, and calibrated uncertainty estimates. Because language‑based representations cannot guarantee these, the paper introduces a new class of systems called Large Quantitative Models (LQMs) that would be trained directly on raw quantitative data to meet these requirements.\n\nBy delineating the gap between linguistic and quantitative modeling, the work calls for a shift in research focus toward architectures and datasets that preserve numeric fidelity, aiming to improve decision‑making reliability in domains where errors carry significant cost.","keyPoints":["LLMs learn from human descriptions, which lose essential quantitative detail, limiting their use in precise decision‑making","The paper defines three required properties—reproducibility, lineage, calibrated uncertainty—that language substrates cannot provide","It proposes a new model class, Large Quantitative Models (LQMs), trained on raw numeric data to meet high‑stakes needs"],"whyItMatters":"Recognizing language’s limits for numeric reasoning pushes the AI field toward data‑centric quantitative models, improving safety and reliability in finance, healthcare, and security.","category":{"slug":"research","name":"Research","url":"https://digestai.news/category/research"},"entities":{"companies":[],"models":[],"people":[]},"firstPublishedAt":"2026-09-14T04:00:00Z","updatedAt":"2026-09-14T04:00:00Z","sourceCount":1,"hasPrimarySource":true,"sources":[{"outlet":"arXiv cs.AI","title":"Language Is an Insufficient Substrate for Quantitative Reasoning, and Consequential Domains Need Large Quantitative Models","url":"https://arxiv.org/abs/2609.12105","publishedAt":"2026-09-14T04:00:00Z","type":"primary","primary":true,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"Language Models Fall Short for Quantitative Decision Tasks, Proposing Large Quantitative Models\", 14 September 2026, https://digestai.news/story/language-models-fall-short-for-quantitative-decision-tasks-proposing-large","publisher":"Digest AI","title":"Language Models Fall Short for Quantitative Decision Tasks, Proposing Large Quantitative Models","datePublished":"2026-09-14T04:00:00Z","url":"https://digestai.news/story/language-models-fall-short-for-quantitative-decision-tasks-proposing-large"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}