CONE: Embeddings for Complex Numerical Data Preserving Unit and Variable Semantics
Large pre-trained models (LMs) and Large Language Models (LLMs) are typically effective at capturing language semantics and contextual relationships. However, these models encounter challenges in maintaining optimal performance on tasks involving numbers. Blindly treating numerical or structured data as terms is inadequate -- their semantics must be well understood and encoded by the models. In this paper, we propose CONE, a hybrid transformer encoder pre-trained model that encodes numbers, ranges, and gaussians into an embedding vector space preserving distance. We introduce a novel composite embedding construction algorithm that integrates numerical values, ranges or gaussians together ...
| Authors | Gyanendra Shrestha, Anna Pyayt, Michael Gubanov |
|---|---|
| Primary category | cs.AI |
| Published | 5 March 2026 |
| Last updated at source | 5 March 2026 |
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