This paper presents DreamLLM, a learning framework that first achieves versatile
Multimodal Large Language Models (MLLMs) empowered with frequently overlooked …

Although instruction-tuned large language models (LLMs) have exhibited remarkable
capabilities across various NLP tasks, their effectiveness on other data modalities beyond …

Large Language Models (LLMs) have garnered significant attention for their advancements
in natural language processing, demonstrating unparalleled prowess in text comprehension …

Responding with multi-modal content has been recognized as an essential capability for an
intelligent conversational agent. In this paper, we introduce the MMDialog dataset to better …

Multimodal learning mines and analyzes multimodal data in reality to better understand and
appreciate the world around people. However, how to exploit this rich multimodal data …

We present EasyGen, an efficient model designed to enhance multimodal understanding
and generation by harnessing the capabilities of diffusion models and large language …

Perceiving multi-modal information and fulfilling dialogues with humans is a long-term goal
of artificial intelligence. Pre-training is commonly regarded as an effective approach for multi …

As sharing images in an instant message is a crucial factor, there has been active research
on learning an image-text multi-modal dialogue models. However, training a well …

Due to the natural multi-modal occurrence format (text, audio, vision) of the dialogues,
textual response generation in dialogues should rely on the multi-modal contexts beyond …

Existing knowledge-grounded conversation systems generate responses typically in a
retrieve-then-generate manner. They require a large knowledge base and a strong …