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Embedding Models Measure in Peculiar Ways

Paper recorded by Signals 4 on 2026-09-17 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-17 on arXiv · recorded by Signals 4 on 2026-09-18

Category: cs.LG · 机器学习 · first seen 2026-09-18

Abstract

Embedding spaces define notions of semantic similarity and distance. We study whether those embeddings reflect physical measurements of mass, distance, time and volume, which admit a unique, objective notion of semantic equivalence and distance. We find that physical measurement is only weakly modeled in the embedding space, and that instead quite peculiar measurement patterns can be observed. Fur

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#1 most recent of 215 cs.LG papers we have recorded · ↓ older: How Does Distribution Shift Shape Pretraining Gains in Neural PDE Surr
Cite this page: Embedding Models Measure in Peculiar Ways: the #1 most recent of 215 cs.LG papers we have recorded (as of 2026-09-17). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/embedding-models-measure-in-peculiar-ways.html
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