CONE: Embeddings for Complex Numerical Data Preserving Unit and Variable Semantics
arXiv:2603.04741v1 Announce Type: new Abstract: 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 with their associated units and attribute names to precisely capture their intricate semantics. We conduct extensive experimental evaluation on large-scale datasets across diverse domains (web, medical, finance, and government) that justifies CONE's stro
arXiv:2603.04741v1 Announce Type: new Abstract: 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 with their associated units and attribute names to precisely capture their intricate semantics. We conduct extensive experimental evaluation on large-scale datasets across diverse domains (web, medical, finance, and government) that justifies CONE's strong numerical reasoning capabilities, achieving an F1 score of 87.28% on DROP, a remarkable improvement of up to 9.37% in F1 over state-of-the-art (SOTA) baselines, and outperforming major SOTA models with a significant Recall@10 gain of up to 25%.
Executive Summary
The proposed CONE model is a significant advancement in the field of natural language processing, particularly in handling numerical data. By incorporating a novel composite embedding construction algorithm, CONE effectively captures the intricate semantics of numbers, ranges, and gaussians while preserving their unit and variable semantics. The model demonstrates strong numerical reasoning capabilities, achieving impressive results on large-scale datasets across diverse domains. This breakthrough has the potential to revolutionize the way language models process numerical data, enabling more accurate and reliable applications in various fields. The significant improvement over state-of-the-art baselines and the model's ability to outperform major SOTA models are testaments to its efficacy. As the model continues to evolve, it is likely to have far-reaching implications for various industries and applications.
Key Points
- ▸ CONE is a hybrid transformer encoder pre-trained model that encodes numbers, ranges, and gaussians into an embedding vector space preserving distance.
- ▸ The model introduces a novel composite embedding construction algorithm that integrates numerical values, ranges, or gaussians with their associated units and attribute names.
- ▸ CONE demonstrates strong numerical reasoning capabilities, achieving an F1 score of 87.28% on DROP, a remarkable improvement of up to 9.37% in F1 over state-of-the-art baselines.
Merits
Strength in Numerical Reasoning
CONE's ability to effectively capture the intricate semantics of numbers, ranges, and gaussians while preserving their unit and variable semantics is a significant strength. This enables the model to achieve impressive results on large-scale datasets across diverse domains.
Improved Performance
CONE outperforms major state-of-the-art models with a significant Recall@10 gain of up to 25%, demonstrating its potential to revolutionize the way language models process numerical data.
Demerits
Limited Domain-Specific Knowledge
While CONE demonstrates strong numerical reasoning capabilities, its performance may be limited when dealing with domain-specific knowledge that requires specialized expertise.
Potential Overfitting
The model's ability to capture intricate semantics may lead to overfitting, particularly when dealing with large and complex datasets.
Expert Commentary
The proposed CONE model is a significant advancement in the field of natural language processing, particularly in handling numerical data. By incorporating a novel composite embedding construction algorithm, CONE effectively captures the intricate semantics of numbers, ranges, and gaussians while preserving their unit and variable semantics. The model demonstrates strong numerical reasoning capabilities, achieving impressive results on large-scale datasets across diverse domains. This breakthrough has the potential to revolutionize the way language models process numerical data, enabling more accurate and reliable applications in various fields. However, the model's performance may be limited when dealing with domain-specific knowledge that requires specialized expertise, and the potential for overfitting needs to be addressed. Nevertheless, CONE is a significant step forward in the field of natural language processing, and its implications are far-reaching.
Recommendations
- ✓ Further research is needed to explore the model's limitations and potential for overfitting.
- ✓ CONE's potential to revolutionize language model processing of numerical data should be explored in various industries, including finance, healthcare, and science.
