Large language models (LLMs) have already revolutionized code generation, after being
pretrained on publicly available code data. However, while various methods have been …

Large language models (LLMs) have demonstrated remarkable potential in handling
multilingual machine translation (MMT). In this paper, we systematically investigate the …

Large Language Models (LLMs) are trained with a pre-defined context length, restricting
their use in scenarios requiring long inputs. Previous efforts for adapting LLMs to a longer …

The scaling of inference computation has unlocked the potential of long-context large
language models (LLMs) across diverse settings. For knowledge-intensive tasks, the …

In-context learning is an emergent paradigm in large language models (LLMs) that enables
them to generalize to new tasks and domains by simply prompting these models with a few …

The recent success of interleaved Large Multimodal Models (LMMs) in few-shot learning
suggests that in-context learning (ICL) with many examples can be promising for learning …

Background The increasing need for diagnostic echocardiography (echo) tests presents
challenges in preserving the quality and promptness of reports. While Large Language …

Large language models (LLMs) excel at few-shot in-context learning (ICL) without requiring
parameter updates. However, as the number of ICL demonstrations increases from a few to …

Large language models (LLMs) have shown remarkable in-context learning (ICL)
capabilities on textual data. We explore whether these capabilities can be extended to …

Broad textual understanding and in-context learning require language models that utilize full
document contexts. Due to the implementation challenges associated with directly training …