Recently, machine learning (ML) methods have been used to create powerful language
models for a broad range of natural language processing tasks. An important subset of this …

Large language models (large LMs) are increasingly trained on massive codebases and
used to generate code. However, LMs lack awareness of security and are found to …

Following the success of GPT4, there has been a surge in interest in multimodal large
language model (MLLM) research. This line of research focuses on developing general …

Aligning language models with preferences can be posed as approximating a target
distribution representing some desired behavior. Existing approaches differ both in the …

LLMs can accomplish specialized medical knowledge tasks, however, equitable access is
hindered by the extensive fine-tuning, specialized medical data requirement, and limited …

As one of the most representative DL techniques, Transformer architecture has empowered
numerous advanced models, especially the large language models (LLMs) that comprise …

Recent studies have explored the potential of large language models (LLMs) for
understanding the semantic information in images. However, the use of LLMs to understand …

The availability of large pre-trained models is changing the landscape of Machine Learning
research and practice, moving from a" training from scratch" to a" fine-tuning''paradigm …

Large pretrained language models generate fluent text but are notoriously hard to
controllably sample from. In this work, we study constrained sampling from such language …

One of the most striking findings in modern research on large language models (LLMs) is
that scaling up compute during training leads to better results. However, less attention has …