Language is an insufficient substrate for quantitative reasoning, and consequential domains need large quantitative models
Read the original at arxiv.org→arXiv:2609.12105v1 Announce Type: new Abstract: The prevailing assumption in applied machine learning is that progress on consequential quantitative decisions such as pricing risk, allocating capital, triaging...
Original headline: "Language Is an Insufficient Substrate for Quantitative Reasoning, and Consequential Domains Need Large Quantitative Models"
Coverage timeline
- Sep 14, 04:00 UTC arXiv cs.AI lead source Language Is an Insufficient Substrate for Quantitative Reasoning, and Consequential Domains Need Large Quantitative Models
- Sep 15, 04:00 UTC arXiv cs.CL Domain-Specific Jargon in Large Language Models: A Comparative Analysis between General-Purpose and Specialist Models
- Sep 16, 04:00 UTC arXiv cs.AI TimeThink: Eliciting Compositional Reasoning in Timeseries Large Language Models