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 …

Connecting text and visual modalities plays an essential role in generative intelligence. For
this reason, inspired by the success of large language models, significant research efforts …

Large vision-language models (LVLMs), designed to interpret and respond to human
instructions, occasionally generate hallucinated or harmful content due to inappropriate …

With the advent and widespread deployment of Multimodal Large Language Models
(MLLMs), the imperative to ensure their safety has become increasingly pronounced …

Multimodal large language models (MLLMs) have shown impressive reasoning abilities,
which, however, are also more vulnerable to jailbreak attacks than their LLM predecessors …

Attracted by the impressive power of Multimodal Large Language Models (MLLMs), the
public is increasingly utilizing them to improve the efficiency of daily work. Nonetheless, the …

In the realm of large vision language models (LVLMs), jailbreak attacks serve as a red-
teaming approach to bypass guardrails and uncover safety implications. Existing jailbreaks …

Aligning large language models (LLMs) with human expectations without human-annotated
preference data is an important problem. In this paper, we propose a method to evaluate the …

Large Language Models (LLMs) face threats from unsafe prompts. Existing methods for
detecting unsafe prompts are primarily online moderation APIs or finetuned LLMs. These …

The emergence of Vision Language Models (VLMs) has brought unprecedented advances
in understanding multimodal information. The combination of textual and visual semantics in …