The advent of large language models (LLMs) revolutionized natural language processing
applications, and running LLMs on edge devices has become increasingly attractive for …

Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks.
However, it requires non-trivial efforts to implement these methods on different models. We …

Recent methodologies in LLM self-training mostly rely on LLM generating responses and
filtering those with correct output answers as training data. This approach often yields a low …

The computational challenges of Large Language Model (LLM) inference remain a
significant barrier to their widespread deployment, especially as prompt lengths continue to …

We present InternLM-XComposer-2.5 (IXC-2.5), a versatile large-vision language model that
supports long-contextual input and output. IXC-2.5 excels in various text-image …

The rapid evolution of Large Language Models (LLMs) has outpaced the development of
model evaluation, highlighting the need for continuous curation of new, challenging …

Rapid advancements have been made in extending Large Language Models (LLMs) to
Large Multi-modal Models (LMMs). However, extending input modality of LLMs to video data …

In the future, large language models (LLMs) may enhance the delivery of healthcare, but
there are risks of misuse. These methods may be trained to allocate resources via unjust …

Large language models (LLMs) have been applied across various intelligent educational
tasks to assist teaching. While preliminary studies have focused on task-specific …

Based on the foundation of Large Language Models (LLMs), Multilingual LLMs (MLLMs)
have been developed to address the challenges faced in multilingual natural language …