Large Language Models (LLMs) have recently transformed both the academic and industrial
landscapes due to their remarkable capacity to understand, analyze, and generate texts …

Large models represent a groundbreaking advancement in multiple application fields,
enabling remarkable achievements across various tasks. However, their unprecedented …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

How to efficiently transform large language models (LLMs) into instruction followers is
recently a popular research direction, while training LLM for multi-modal reasoning remains …

Multitask prompted finetuning (MTF) has been shown to help large language models
generalize to new tasks in a zero-shot setting, but so far explorations of MTF have focused …

Despite the advancements of open-source large language models (LLMs), eg, LLaMA, they
remain significantly limited in tool-use capabilities, ie, using external tools (APIs) to fulfill …

Fine-tuning on instruction data has been widely validated as an effective practice for
implementing chat language models like ChatGPT. Scaling the diversity and quality of such …

Prompting has shown impressive success in enabling large pretrained language models
(LMs) to perform diverse NLP tasks, especially when only few downstream data are …

Fine-tuning language models (LMs) has yielded success on diverse downstream tasks, but
as LMs grow in size, backpropagation requires a prohibitively large amount of memory …

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 …