With the rising complexity and capability of Artificial Intelligence (AI), its latest innovation, i.e., the Large Language Models (LLMs), has demonstrated great advances in tasks, including text generation, language translation, text summarization, and code...

In recent years, researchers in the field of robotic reinforcement learning (RL) have achieved significant progress, developing methods capable of handling complex image observations, training in real-world scenarios, and incorporating auxiliary data, such as...

Large language models (LLMs) have profoundly transformed the landscape of artificial intelligence (AI) in natural language processing (NLP). These models can understand and generate human-like text, representing a pinnacle of current AI research. Yet, the...

The emergence of Large Language Models (LLMs) has transformed the landscape of natural language processing (NLP). The introduction of the transformer architecture marked a pivotal moment, ushering in a new era in NLP. While a universal definition for LLMs is lacking,...

Self-supervised learning (SSL) has proven to be an indispensable technique in AI, particularly in pretraining representations on vast, unlabeled datasets. This significantly reduces the dependency on labeled data, often a major bottleneck in machine learning. Despite...

Generative AI has recently seen a boom, with large language models (LLMs) showing broad applicability across many fields. These models have improved the performance of numerous tools, including those that facilitate interactions based on searches, program synthesis,...