Training on high-quality synthetic data from strong language models (LMs) is a common
strategy to improve the reasoning performance of LMs. In this work, we revisit whether this …

We present MM1. 5, a new family of multimodal large language models (MLLMs) designed
to enhance capabilities in text-rich image understanding, visual referring and grounding …

When mobile meets LLMs, mobile app users deserve to have more intelligent usage
experiences. For this to happen, we argue that there is a strong need to apply LLMs for the …

Generative AI is transforming enterprise application development by enabling machines to
create content, code, and designs. These models, however, demand substantial …

Mobile screen assistants help smartphone users by interpreting mobile screens and
responding to user requests. The excessive private information on mobile screens …

Finding the optimal learning rate for language model pretraining is a challenging task. This
is not only because there is a complicated correlation between learning rate, batch size …

Fine-tuning large language models (LLMs) with low-rank adaptations (LoRAs) has become
common practice, often yielding numerous copies of the same LLM differing only in their …

With the rapid scaling of large language models (LLMs), structured pruning has become a
widely used technique to learn efficient, smaller models from larger ones, delivering superior …

Augmenting large language models (LLMs) with external tools is a promising approach to
enhance their capabilities. Effectively leveraging this potential for complex tasks hinges …

Reconfigurable processor-based acceleration of deep convolutional neural network (DCNN)
algorithms has emerged as a widely adopted technique, with particular attention on sparse …