The word embedding space in neural models is skewed, and correcting this can improve task performance.

Large language models (LLMs), typically designed as a function of next-word prediction, have excelled across extensive NLP tasks. Despite the generality, next-word prediction is often not an efficient formulation for many of the tasks, demanding an extreme scale of model parameters (10s or 100s of billions) and sometimes yielding suboptimal performance. In practice, it is often desirable to build more efficient models--despite being less versatile, they still apply to a substantial subset of problems, delivering on par or even superior performance with much smaller model sizes.

Regularization is one of the most fundamental topics in optimization, statistics and machine learning.

Recently, large language models (LLMs) have achieved superior performance, empowering the development of large multimodal agents (LMAs).