Large Language Models (LLMs) have emerged as a transformative force, revolutionizing
numerous fields well beyond the conventional domain of Natural Language Processing …

While recently Multimodal Large Language Models (MM-LLMs) have made exciting strides,
they mostly fall prey to the limitation of only input-side multimodal understanding, without the …

Integrating large language models (LLMs) with various tools has led to increased attention
in the field. Existing approaches either involve fine-tuning the LLM, which is both …

Instruction Tuning has the potential to stimulate or enhance specific capabilities of large
language models (LLMs). However, achieving the right balance of data is crucial to prevent …

While large language models (LMs) have shown remarkable capabilities across numerous
tasks, they often struggle with simple reasoning and planning in physical environments …

The introduction of ChatGPT has led to a significant increase in the utilization of Large
Language Models (LLMs) for addressing downstream tasks. There's an increasing focus on …

While training large language models (LLMs) from scratch can generate models with distinct
functionalities and strengths, it comes at significant costs and may result in redundant …

Multimodal Large Language Models (MLLMs) have shown impressive abilities in interacting
with visual content with myriad potential downstream tasks. However, even though a list of …

This paper presents novel systems and methodologies for the development of efficient large
language models (LLMs). It explores the trade-offs between model size, performance, and …

The growing interest in vision-language models (VLMs) has been driven by improvements in
large language models and vision transformers. Despite the abundance of literature on this …