How do large language models (LLMs) develop and evolve over the course of training?
How do these patterns change as models scale? To answer these questions, we introduce …

Large language models (LLMs) show excellent performance but are compute-and memory-
intensive. Quantization can reduce memory and accelerate inference. However, existing …

The increasing fluency and widespread usage of large language models (LLMs) highlight
the desirability of corresponding tools aiding detection of LLM-generated text. In this paper …

This paper explores the academic integrity considerations of students' use of Artificial
Intelligence (AI) tools using Large Language Models (LLMs) such as ChatGPT in formal …

Although large language models often produce impressive outputs, it remains unclear how
they perform in real-world scenarios requiring strong reasoning skills and expert domain …

Large language models can perform various reasoning tasks by using chain-of-thought
prompting, which guides them to find answers through step-by-step demonstrations …

Given the recent impressive accomplishments of language models (LMs) for code
generation, we explore the use of LMs as general adaptive mutation and crossover …

We study recent research advances that improve large language models through efficient
pre-training and scaling, and open datasets and tools. We combine these advances to …

The computation necessary for training Transformer-based language models has
skyrocketed in recent years. This trend has motivated research on efficient training …

We present MM-VID, an integrated system that harnesses the capabilities of GPT-4V,
combined with specialized tools in vision, audio, and speech, to facilitate advanced video …