By simply composing prompts, developers can prototype novel generative applications with
Large Language Models (LLMs). To refine prototypes into products, however, developers …

Prompt-based interfaces for Large Language Models (LLMs) have made prototyping and
building AI-powered applications easier than ever before. However, identifying potential …

Advancements in large language models (LLMs) are poised to spark a proliferation of LLM-
powered user experiences. In product teams, designers are often tasked with crafting user …

Machine learning models often learn latent embedding representations that capture the
domain semantics of their training data. These embedding representations are valuable for …

Commit messages play a vital role in software development and maintenance. While
previous research has introduced various Commit Message Generation (CMG) approaches …

Label quality issues, such as noisy labels and imbalanced class distributions, have negative
effects on model performance. Automatic reweighting methods identify problematic samples …

Though ML practitioners increasingly employ various Responsible ML (RML) strategies,
their methodological approach in practice is still unclear. In particular, the constraints …

Though ML practitioners increasingly employ various Responsible ML (RML) strategies,
their methodological approach in practice is still unclear. In particular, the constraints …

To deploy machine learning models on-device, practitioners use compression algorithms to
shrink and speed up models while maintaining their high-quality output. A critical aspect of …

In the applications of machine learning, it is difficult to ensure the quality of training data due
to the various sources of training data and the inexperience of some annotators. By tightly …