Large Language Models (LLMs) demonstrate impressive capabilities across various fields,
yet their increasing use raises critical security concerns. This article reviews recent literature …

This survey paper examines the recent advancements in AI agent implementations, with a
focus on their ability to achieve complex goals that require enhanced reasoning, planning …

The past year has seen a steep rise in generative AI systems that claim to be open. But how
open are they really? The question of what counts as open source in generative AI is poised …

The computational complexity of the self-attention mechanism in popular transformer
architectures poses significant challenges for training and inference, and becomes the …

Combining Large Language Models (LLMs) with search engine services marks a significant
shift in the field of services computing, opening up new possibilities to enhance how we …

We present ShieldGemma, a comprehensive suite of LLM-based safety content moderation
models built upon Gemma2. These models provide robust, state-of-the-art predictions of …

Large language models (LLMs) have achieved success in acting as agents, which interact
with environments through tools such as search engines. However, LLMs are optimized for …

Large language model behavior is shaped by the language of those with whom they
interact. This capacity and their increasing prevalence online portend that they will …

Unstructured data formats account for over 80% of the data currently stored, and extracting
value from such formats remains a considerable challenge. In particular, current approaches …

To perform effective causal inference in high-dimensional datasets, initiating the process
with causal discovery is imperative, wherein a causal graph is generated based on …