语言不足以支撑定量推理，关键领域需要大型定量模型
原标题：Language Is an Insufficient Substrate for Quantitative Reasoning, and Consequential Domains Need Large Quantitative Models
AI 摘要
这篇 arXiv 立场论文认为，语言模型基于人类描述训练，而描述是定量记录的有损编码，因此无法胜任定价风险、资本配置、患者分诊、入侵检测等关键定量决策。作者提出「大型定量模型（LQM）」这一新模型类别，要求原生定量数据训练、显式可检查表示、完整溯源和校准不确定性，并主张语言模型只应充当人机接口。
正文节选
Language Is an Insufficient Substrate for Quantitative Reasoning, and Consequential Domains Need Large Quantitative Models Abstract The prevailing assumption in applied machine learning is that progress on consequential quantitative decisions such as pricing risk, allocating capital, triaging patients, or containing a network intrusion will follow from progress in large language models (LLMs). This paper takes the position that the assumption is mistaken, and that the mistake is structural rathe