Recent advancements in large language models (LLMs) have transformed the field of
question answering (QA). However, evaluating LLMs in the medical field is challenging due …

Today's LLMs are susceptible to prompt injections, jailbreaks, and other attacks that allow
adversaries to overwrite a model's original instructions with their own malicious prompts. In …

Recent advances in Large Language Models (LLMs) enable exciting LLM-integrated
applications, which perform text-based tasks by utilizing their advanced language …

Large Language Model (LLM) systems are inherently compositional, with individual LLM
serving as the core foundation with additional layers of objects such as plugins, sandbox …

Large language models (LLMs) have exhibited great potential in autonomously completing
tasks across real-world applications. Despite this, these LLM agents introduce unexpected …

The deployment of multimodal large language models (MLLMs) has brought forth a unique
vulnerability: susceptibility to malicious attacks through visual inputs. We delve into the novel …

Recent work has embodied LLMs as agents, allowing them to access tools, perform actions,
and interact with external content (eg, emails or websites). However, external content …

Intelligent agents powered by large language models (LLMs) have demonstrated substantial
promise in autonomously conducting experiments and facilitating scientific discoveries …

LLMs have becoming increasingly powerful, both in their benign and malicious uses. With
the increase in capabilities, researchers have been increasingly interested in their ability to …

Multimodal Large Language Models (MLLMs) excel in generating responses based on
visual inputs. However, they often suffer from a bias towards generating responses similar to …