Large language model (LLM) based agents have recently attracted much attention from the
research and industry communities. Compared with original LLMs, LLM-based agents are …

Large language models (LLMs) are commonly trained on datasets consisting of fixed-length
token sequences. These datasets are created by randomly concatenating documents of …

State-of-the-art medical multi-modal large language models (med-MLLM), like LLaVA-Med
or BioMedGPT, leverage instruction-following data in pre-training. However, those models …

We address the challenge of representing long captions in vision-language models, such as
CLIP. By design these models are limited by fixed, absolute positional encodings, restricting …

Large Language Models (LLMs) have been well-researched in many long-context tasks.
However, due to high annotation costs, high-quality long-context summary datasets for …

Recent advancements in Large Language Models (LLMs) have significantly enhanced their
ability to process long contexts, yet a notable gap remains in generating long, aligned …

Large language models (LLMs) exhibit outstanding performance in machine translation via
in-context learning. In contrast to sentence-level translation, document-level translation …

Biomedical imaging modalities often produce high-resolution, multi-dimensional images that
pose computational challenges for deep neural networks. These computational challenges …

Data-serialization libraries are essential tools in software development, responsible for
converting between programmable data structures and data persistence formats. Among …

We survey the large language model (LLM) serving area to understand the intricate
dynamics between cost-efficiency and accuracy, which is magnified by the growing need for …