In machine learning, the effectiveness of tree ensembles, such as random forests, has long been acknowledged. These ensembles, which pool the predictive power of multiple decision trees, stand out for their remarkable accuracy across various applications. This work,...

The emergence of Large Language Models (LLMs) has notably enhanced the domain of computational linguistics, particularly in multi-agent systems. Despite the significant advancements, developing multi-agent applications remains a complex endeavor. This complexity...

The advent of transformer architectures has marked a significant milestone, particularly in their application to in-context learning. These models can make predictions based solely on the information presented within the input sequence without explicit parameter...

Large Language Models (LLMs) have significantly evolved in recent times, especially in the areas of text understanding and generation. However, there have been certain difficulties in optimizing LLMs for more effective human instruction delivery. While LLMs have shown...

Recent advancements in (self) supervised learning models have been driven by empirical scaling laws, where a model’s performance scales with its size. However, such scaling laws have been challenging to establish in reinforcement learning (RL). Unlike supervised...

In the evolving landscape of psycholinguistics, language models (LMs) have carved out a pivotal role, serving as both the subject and tool of study. These models, leveraging vast datasets, attempt to mimic human language processing capabilities, offering invaluable...