In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-
of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In this new …

Large language models (LLMs) have demonstrated extraordinary capabilities and
contributed to multiple fields, such as generating and summarizing text, language …

Large language models (LLMs) have shown promise in medical question answering, with
Med-PaLM being the first to exceed a 'passing'score in United States Medical Licensing …

In this paper, we introduce InternVL 1.5, an open-source multimodal large language model
(MLLM) to bridge the capability gap between open-source and proprietary commercial …

In this work, we discuss building performant Multimodal Large Language Models (MLLMs).
In particular, we study the importance of various architecture components and data choices …

As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can
offer reliable and up-to-date external knowledge, providing huge convenience for numerous …

The recent surge of Multimodal Large Language Models (MLLMs) has fundamentally
reshaped the landscape of AI research and industry, shedding light on a promising path …

Visual encoding constitutes the basis of large multimodal models (LMMs) in understanding
the visual world. Conventional LMMs process images in fixed sizes and limited resolutions …

While recent preference alignment algorithms for language models have demonstrated
promising results, supervised fine-tuning (SFT) remains imperative for achieving successful …

Abstract Information Extraction (IE) aims to extract structural knowledge from plain natural
language texts. Recently, generative Large Language Models (LLMs) have demonstrated …