We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion
tokens, whose overall performance, as measured by both academic benchmarks and …

Large language models (LLMs) are increasingly adopted in educational contexts to provide
personalized support to students and teachers. The unprecedented capacity of LLM‐based …

While large language models (LLMs) have demonstrated remarkable capabilities across a
range of downstream tasks, a significant concern revolves around their propensity to exhibit …

Recently, large language models (LLMs) have attracted considerable attention due to their
remarkable capabilities. However, LLMs' generation of biased or hallucinatory content …

Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks.
However, it requires non-trivial efforts to implement these methods on different models. We …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

In this paper, we introduce LLaVA-φ (LLaVA-Phi), an efficient multi-modal assistant that
harnesses the power of the recently advanced small language model, Phi-2, to facilitate …

Large Language Models (LLMs) have drawn a lot of attention due to their strong
performance on a wide range of natural language tasks, since the release of ChatGPT in …

We present a unified transformer, ie, Show-o, that unifies multimodal understanding and
generation. Unlike fully autoregressive models, Show-o unifies autoregressive and …

Within the evolving landscape of deep learning, the dilemma of data quantity and quality has
been a long-standing problem. The recent advent of Large Language Models (LLMs) offers …