语言不足以作为定量推理的基底，且后果性领域需要大型定量模型
Language Is an Insufficient Substrate for Quantitative Reasoning, and Consequential Domains Need Large Quantitative Models
- Duo Dimensio LLC（Duo Dimensio 有限责任公司）
- Indiana University（印第安纳大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文指出语言模型因描述性表征的不可逆损失，无法满足后果性定量决策所需的可复现性、血统和校准不确定性，提出大型定量模型（LQM）作为新类别。
AI中文摘要：
应用机器学习中的主流假设是，在定价风险、配置资本、对患者进行分诊或遏制网络入侵等后果性定量决策上的进展，将随着大型语言模型（LLMs）的进步而实现。语言模型是在由人类描述产生的世界表征上进行训练的；描述是定量记录的损失性编码，且这种损失是不可逆的：任何规模的下游模型都无法从描述中恢复描述未编码的内容。我们将此形式化为模型训练所基于的表征的属性，而非模型能力的属性，并识别出后果性场景对模型要求的另外三个属性，而语言基底在构造上无法提供这些属性：可复现性、从每个输出回溯到产生它的源记录的血统，以及校准的不确定性。我们认为这些属性定义了一个独特的模型类别，我们称之为大型定量模型（LQM）。
英文摘要：
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). A language model is trained on a representation of the world that was produced by human description; description is a lossy encoding of the quantitative record, and the loss is irreversible: no downstream model, at any scale, can recover from a description what the description did not encode. We formalize this as a property of the representation on which a model is trained rather than of the model capacity, and we identify three further properties that consequential settings demand of a model and that a language substrate cannot supply by construction: reproducibility, lineage from every output back to the source records that produced. it, and calibrated uncertainty. We argue that these properties define a distinct model class, which we call the Large Quantitative Model (LQM).
