Large Language Models (LLMs) have the capacity to store and recall facts. Through
experimentation with open-source models, we observe that this ability to retrieve facts can …

Large language models (LLMs) have proven to be remarkably efficient, both across a wide
range of natural language processing tasks and well beyond them. However, a …

Chain-of-Thought (CoT) is an efficient prompting method that enables the reasoning ability
of large language models by augmenting the query using multiple examples with …

The transformer architecture has catalyzed revolutionary advances in language modeling.
However, recent architectural recipes, such as state-space models, have bridged the …

Attention mechanisms have revolutionized several domains of artificial intelligence, such as
natural language processing and computer vision, by enabling models to selectively focus …

Chain-of-Thought (CoT) is an efficient prompting method that enables the reasoning ability
of large language models by augmenting the query using multiple examples with multiple …

In recent years, transformer-based models have revolutionized deep learning, particularly in
sequence modeling. To better understand this phenomenon, there is a growing interest in …

In the field of formal verification, Neural Networks (NNs) are typically reformulated into
equivalent mathematical programs which are optimized over. To overcome the inherent non …