Instruction tuning plays a critical role in aligning large language models (LLMs) with human
preference. Despite the vast amount of open instruction datasets, naively training a LLM on …

Large vision-language models (LVLMs) have achieved impressive results in various visual
question-answering and reasoning tasks through vision instruction tuning on specific …

Autoregressive next-token prediction is a standard pretraining method for large-scale
language models, but its application to vision tasks is hindered by the non-sequential nature …

Computer vision has advanced research methodologies, enhancing system services across
various fields. It is a core component in traffic monitoring systems for improving road safety; …

This article explores deep learning models for the automatic identification of registers-text
varieties such as news reports and discussion forums-in web-based datasets across 16 …

In this study, we uncover the unexpected efficacy of residual-based large language models
(LLMs) as part of encoders for biomedical imaging tasks, a domain traditionally devoid of …

This position paper proposes a data-centric viewpoint of AI research, focusing on large
language models (LLMs). We start by making a key observation that data is instrumental in …

Data plays a fundamental role in training Large Language Models (LLMs). Efficient data
management, particularly in formulating a well-suited training dataset, is significant for …

This position paper proposes a data-centric viewpoint of AI research, focusing on large
language models (LLMs). We start by making the key observation that data is instrumental in …

The essence of sequential recommender systems (RecSys) lies in understanding how users
make decisions. Most existing approaches frame the task as sequential prediction based on …