Large Language Models (LLMs) are swiftly advancing in architecture and capability, and as
they integrate more deeply into complex systems, the urgency to scrutinize their security …

As large language models continue to develop in the field of AI, text generation systems are
susceptible to a worrisome phenomenon known as hallucination. In this study, we …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

While large language models (LLMs) have demonstrated remarkable capabilities across a
range of downstream tasks, a significant concern revolves around their propensity to exhibit …

We posit that to achieve superhuman agents, future models require superhuman feedback
in order to provide an adequate training signal. Current approaches commonly train reward …

Large Language Models (LLMs) trained on extensive textual corpora have emerged as
leading solutions for a broad array of Natural Language Processing (NLP) tasks. Despite …

We introduce" HallusionBench" a comprehensive benchmark designed for the evaluation of
image-context reasoning. This benchmark presents significant challenges to advanced large …

Nowadays, open-source large language models like LLaMA have emerged. Recent
developments have incorporated supervised fine-tuning (SFT) and reinforcement learning …

Large language models (LLMs), after being aligned with vision models and integrated into
vision-language models (VLMs), can bring impressive improvement in image reasoning …

Alignment tuning has become the de facto standard practice for enabling base large
language models (LLMs) to serve as open-domain AI assistants. The alignment tuning …