One of the most striking findings in modern research on large language models (LLMs) is
that scaling up compute during training leads to better results. However, less attention has …

While Large Vision-Language Models (LVLMs) have rapidly advanced in recent years, the
prevalent issue known as thehallucination'problem has emerged as a significant bottleneck …

Despite the astonishing performance of recent Large Vision-Language Models (LVLMs),
these models often generate inaccurate responses. To address this issue, previous studies …

We propose a new framework that automatically generates high-quality segmentation masks
with their referring expressions as pseudo supervisions for referring image segmentation …

General-purpose large language models (LLMs) are progressively expanding both in scale
and access to unpublic training data. This has led to notable progress in a variety of AI …

With the widespread application of Large Language Models (LLMs), it has become a
significant concern to ensure their safety and prevent harmful responses. While current safe …

Despite the remarkable strides made by autoregressive language models, their potential is
often hampered by the slow inference speeds inherent in sequential token generation …

This paper presents a way of enhancing the reliability of Large Multimodal Models (LMMs) in
addressing hallucination effects, where models generate incorrect or unrelated responses …

Reinforcement learning from human feedback (RLHF) has been crucial in aligning large
language models (LLMs) with human values. Traditionally, RLHF involves generating …

Multi-modal Large Language Models (MLLMs) demonstrate remarkable success across
various vision-language tasks. However, they suffer from visual hallucination, where the …