Distance Matters: Euclidean Embedding Distances for Improved Language Model Generalization and Adaptability

Distance Matters: Euclidean Embedding Distances for Improved Language Model Generalization and Adaptability

Large language models (LLMs) have revolutionized natural language processing (NLP), enabling machines to process, understand and generate human-like text with high accuracy. However, the current practices in training and evaluating these models often overlook the relationship between the embeddings...

Full description

Bibliographic Details
Main Author: Sultan Alshamrani
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Language models
natural language processing
embeddings
model generalization
model robustness
model performance
Online Access:https://ieeexplore.ieee.org/document/10613752/

Internet

https://ieeexplore.ieee.org/document/10613752/

Similar Items