Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

The recent success of large language models (LLMs) trained on static, pre-collected,
general datasets has sparked numerous research directions and applications. One such …

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 …

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 …

Large language models (LLMs) have shown impressive performance on complex reasoning
by leveraging chain-of-thought (CoT) prompting to generate intermediate reasoning chains …

In the quest for artificial general intelligence, Multi-modal Large Language Models (MLLMs)
have emerged as a focal point in recent advancements. However, the predominant focus …

This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …

The needle-in-a-haystack (NIAH) test, which examines the ability to retrieve a piece of
information (the" needle") from long distractor texts (the" haystack"), has been widely …

Transformer-based large language models (LLMs) demonstrate impressive performance
across various natural language processing tasks. Serving LLM inference for generating …

Large language models (LLMs) excel at few-shot in-context learning (ICL)--learning from a
few examples provided in context at inference, without any weight updates. Newly expanded …